" (parameter depends on the "AT+CMGF" setting, PDU or text mode) command is sent by the originating TE. |
+| Successfully received SMS | Stop:
The short message is received by the B-party's UE. | Stop:
Layer 3: The "Message transfer" is received in the terminating UE (Figure 7-61, signalling point number 6).
Detailed: CP_DATA (RP_ACK) is sent by the terminating UE.
AT: The "+CMTT" event received at the terminating TE. |
+| Unsuccessfully received SMS | Stop trigger point not reached or SMS received is duplicated or corrupted. | |
+
+
+
+```
+
+sequenceDiagram
+ participant SC
+ participant SMS-GMSC
+ participant HLR
+ participant MSC or SGSN
+ participant VLR
+ participant MS
+
+ Note right of MS: E.804(14)_F7-61
+
+ SC->>SMS-GMSC: 1a. Message transfer
+ SMS-GMSC->>HLR: 2. SendRoutingInfo-ForShortMsg
+ HLR->>MSC or SGSN: 4a. ForwardShortMessage
+ MSC or SGSN->>VLR: 5. sendInfoForMT-SMS (Note)
+ VLR->>MS: 6. Message transfer
+ MS-->>MSC or SGSN: 4b. Delivery report
+ MSC or SGSN->>HLR: 3. SM-Delivery ReportStatus
+ HLR-->>SMS-GMSC: 1b. Delivery report
+
+```
+
+→ Operation invocation or message transfer
+ ↔ Successful operation invocation or message transfer including report
+
+Sequence diagram of SMS transaction flow for terminating UE. Lifelines: SC, SMS-GMSC, HLR, MSC or SGSN, VLR, MS. The sequence shows message transfer from SC to SMS-GMSC, routing info request from SMS-GMSC to HLR, forward short message from HLR to MSC/SGSN, send info for MT-SMS from MSC/SGSN to VLR, message transfer from VLR to MS, delivery report from MS to MSC/SGSN, SM-Delivery ReportStatus from MSC/SGSN to HLR, delivery report from HLR to SMS-GMSC, and finally delivery report from SMS-GMSC to SC.
+
+NOTE – This operation is not used by the SGSN.
+
+**Figure 7-61 – SMS transaction flow –Terminating UE**
+
+TETRA:
+
+| Event from abstract equation | Trigger point from user's point of view | Technical description/ protocol part |
+|------------------------------|--------------------------------------------------------|--------------------------------------|
+| SDS service attempt | Start: Push the send button (initiate sending an SDS). | Start: |
+
+| Event from abstract equation | Trigger point from user's point of view | Technical description/ protocol part |
+|----------------------------------|----------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| | | Layer 3 (CMCE): The first "U-SDS-DATA" message is sent by the originating UE.
AT: The "AT+CMGS="<called party identity>", <length><CR> <LF><user data><CtrlZ>" command is sent by the originating TE, where <called party identity> provides a unique identification of the desired B-party and <length> is the size of the SDS in [bits].
|
+| Successfully received test SDS | Stop: The short message is received by the terminating party. | Stop:
Layer 3 (CMCE): The corresponding "D-SDS-DATA" message is received by the terminating UE.
AT: The "AT+CMTI" new message indication or the "AT+CTSDSR" receive notification for the corresponding message is received at the terminating TE (depending on SDS settings).
|
+| Unsuccessfully received test SDS | Stop trigger point not reached or SMS received is duplicated or corrupted. | |
+
+Remarks:
+
+- In GSM/UMTS, "CMGR=" gives back the received SMS, or "CMGL="ALL" or "CMGL=4" all of the received ones. In order to receive CMTI events they have to be activated at the B-party with the "AT+CNMI" command.
+- The detection of duplicated and corrupted received SMS or SDS is a post processing issue.
+- In TETRA, the short data service centre (SDSC) might modify the content of an SDS. The unique identification of an SDS at the receiving UE is up to the following analysis.
+
+#### 7.4.4.5 {SMS | SDS} end-to-end delivery time [s]
+
+##### 7.4.4.5.1 Abstract definition
+
+The {SMS | SDS} end-to-end delivery time is the time period between sending a short message to the network and receiving the very same short message at another UE.
+
+##### 7.4.4.5.2 Abstract equation
+
+$$\{ \text{SMS} | \text{SDS} \} \text{ end - to - end delivery time [s]} = (t_{\text{B,receive}} - t_{\text{A,send}}) \text{ [s]}$$
+
+##### 7.4.4.5.3 Trigger points
+
+GSM/UMTS:
+
+| Event from abstract equation | Trigger point from user's point of view | Technical description/ protocol part |
+|------------------------------|-----------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------|
+| $t_{\text{A,send}}$ | Start:
Push the send button (Initiate sending an SMS). | Start:
Layer 3: The first "Access request" is sent by the originating UE (Figure 7-59, most upper signalling point). |
+
+| Event from abstract equation | Trigger point from user's point of view | Technical description/ protocol part |
+|------------------------------|-------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| | | Detailed: CM service request is sent by the originating UE.
AT: The "AT+CMGS=" or "AT+CMGS=" (parameter depends on the "AT+CMGF" setting, PDU or text mode) command is sent by the originating TE. |
+| $t_{B,receive}$ | Stop:
The short message is received by the B-party's UE. | Stop:
Layer 3: The "Message transfer" is received by the terminating UE (Figure 7-61, signalling point number 6).
Detailed: CP_DATA (RP_ACK) is sent by the terminating UE.
AT: The "CMTI" event received at the terminating TE. |
+
+TETRA:
+
+| Event from abstract equation | Trigger point from user's point of view | Technical description/ protocol part |
+|------------------------------|---------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| $t_{A,send}$ | Start: Push the send button (initiate sending an SDS). | Start:
Layer 3 (CMCE): The first "U-SDS-DATA" message is sent by the originating UE.
AT: The "AT+CMGS="", " command is sent by the originating TE, where provides a unique identification of the desired B-party and is the size of the SDS in [bits]. |
+| $t_{B,receive}$ | Stop: The short message is received by the terminating party. | Stop:
Layer 3 (CMCE): The corresponding "D-SDS-DATA" message is received by the terminating UE.
AT: The "AT+CMTI" new message indication or the "AT+CTSDSR" receive notification for the corresponding message is received at the terminating TE (depending on SDS settings). |
+
+#### 7.4.4.6 {SMS | SDS} receive confirmation failure ratio [%]
+
+##### 7.4.4.6.1 Abstract definition
+
+The {SMS | SDS} receive confirmation failure ratio is the probability that the receive confirmation for a sent attempt is not received by the originating UE although requested.
+
+##### 7.4.4.6.2 Abstract equation
+
+$$\{SMS | SDS\} \text{ receive confirmation failure Ratio} [\%] = \frac{\text{non-confirmed} \{SMS | SDS\} \text{ receptions}}{\text{all} \{SMS | SDS\} \text{ service attempts}} \times 100$$
+
+#### 7.4.4.6.3 Trigger points
+
+GSM/UMTS:
+
+| Event from abstract equation | Trigger point from user's point of view | Technical description/ protocol part |
+|------------------------------|---------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| SMS service attempt | Start: Push the send button (initiate sending an SMS). | Start:
Layer 3: The first "Access request" is sent by the originating UE (Figure 7-59, most upper signalling point).
Detailed: CM service request is sent by the originating UE.
AT: The "AT+CMGS=" or "AT+CMGS=" (parameter depends on the "AT+CMGF" setting, PDU or text mode) command is sent by the originating TE. |
+| Confirmed SMS reception | Stop: Receive the confirmation at the initiating party that the message is received at the terminating party. | Stop:
To be defined. |
+| Non-confirmed SMS reception | Stop trigger point not reached. | |
+
+TETRA:
+
+| Event from abstract equation | Trigger point from user's point of view | Technical description/ protocol part |
+|------------------------------|---------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| SDS service attempt | Start: Push the send button (initiate sending an SDS). | Start:
Layer 3 (CMCE): The first "U-SDS-DATA" message carrying the "SDS-TRANSFER" message with Delivery report request "Message received report requested" or "Message received and consumed report requested" is sent by the originating UE.
AT: The "AT+CMGS="", " command is sent by the originating TE. |
+| Confirmed SMS reception | Stop: Receive the confirmation at the initiating party that the message is received at the terminating party. | Stop:
Layer 3 (CMCE): The "D-SDS-DATA" message carrying the "SDS-REPORT" message with delivery status "SDS receipt acknowledged by destination" is received by the originating UE.
AT: to be defined. |
+| Non-conformed SMS reception | Stop trigger point not reached. | |
+
+Figure 7-62 shows SDS signalling flow chart for SDS type 4.
+
+
+
+Signalling flow chart for SDS type 4. The diagram shows the interaction between an Application, SDS-TL, SwMI, SDS-TL, and Application. The sequence of messages is: 1. Application sends 'tlstds\_transfer req' to SDS-TL. 2. SDS-TL sends 'SDS-TRANSFER' to SwMI. 3. SwMI sends 'SDS-TRANSFER' to SDS-TL. 4. SDS-TL sends 'tlstds\_transfer ind' to Application. 5. Application sends 'tlstds\_tnsds\_report ind (message successfully transferred to SwMI)' to SDS-TL. 6. SDS-TL sends 'SDS-REPORT' to SwMI. 7. SwMI sends 'SDS-REPORT' to SDS-TL. 8. SDS-TL sends 'tlstds\_report ind (message received by destination)' to Application. 9. Application sends 'tlstds\_report req' to SDS-TL. 10. SDS-TL sends 'SDS-REPORT' to SwMI. 11. SwMI sends 'SDS-REPORT' to SDS-TL. 12. SDS-TL sends 'tlstds\_report ind (message consumed by destination)' to Application. 13. Application sends 'tlstds\_ack\_req' to SDS-TL. 14. SDS-TL sends 'SDS-ACK' to SwMI. 15. SwMI sends 'SDS-ACK' to SDS-TL. 16. SDS-TL sends 'tlstds\_ack\_ind SDS consumed report acknowledgement' to Application. Two dashed boxes on the right indicate optional steps: 'If 'message received' requested' (steps 8-9) and 'If 'message consumed' requested' (steps 12-13). The diagram is labeled E.804(14)\_F7-62.
+
+NOTE 1 – SDS-TL protocol according to [ETSI EN 300 392-2], clause 29.3.3 with transparent SwMI transport is used
+
+NOTE 2 – SwMI store-and-forward functionality uses additional SDS-REPORT and SDS-ACK messages
+
+**Figure 7-62 – Signalling flow chart for SDS type 4**
+
+#### 7.4.4.7 {SMS | SDS} receive confirmation time [s]
+
+##### 7.4.4.7.1 Abstract definition
+
+The {SMS | SDS} receive confirmation time is the time period between sending a short message to the network and receiving the receive confirmation for this message from the network.
+
+##### 7.4.4.7.2 Abstract equation
+
+$$\{ \text{SMS} | \text{SDS} \} \text{ receive confirmation time [s]} = (t_{\text{A, receive confirmation}} - t_{\text{A, send}}) \text{ [s]}$$
+
+### 7.4.4.7.3 Trigger points
+
+GSM/UMTS:
+
+| Event from abstract equation | Trigger point from user's point of view | Technical description/ protocol part |
+|-------------------------------|---------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| $t_{A,send}$ | Start: Push the send button (initiate sending an SMS). | Start:
Layer 3: The first "Access request" is sent by the originating UE (Figure 7-59, most upper signalling point).
Detailed: CM service request is sent by the originating UE.
AT: The "AT+CMGS=" or "AT+CMGS=" (parameter depends on the "AT+CMGF" setting, PDU or text mode) command is sent by the originating TE. |
+| $t_{A,receive\ confirmation}$ | Stop: Receive the confirmation at the initiating party that the message is received at the terminating party. | Stop:
To be defined. |
+
+TETRA:
+
+| Event from abstract equation | Trigger point from user's point of view | Technical description/ protocol part |
+|-------------------------------|---------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| $t_{A,send}$ | Start: Push the send button (initiate sending an SDS). | Start:
Layer 3 (CMCE): The first "U-SDS-DATA" message carrying the "SDS-TRANSFER" message with delivery report request "Message received report requested" or "Message received and consumed report requested" is sent by the originating UE.
AT: The "AT+CMGS=", " command is sent by the originating TE. |
+| $t_{A,receive\ confirmation}$ | Stop: Receive the confirmation at the initiating party that the message is received at the terminating party. | Stop:
Layer 3 (CMCE): The "D-SDS-DATA" message carrying the "SDS-REPORT" message with delivery status "SDS receipt acknowledged by destination" is received by the originating UE.
AT: to be defined. |
+
+### 7.4.4.8 {SMS | SDS} consumed confirmation failure ratio [%]
+
+#### 7.4.4.8.1 Abstract definition
+
+The {SMS | SDS} consumed confirmation failure ratio is the probability that the consumed confirmation for a sent attempt is not received by the originating UE although requested.
+
+#### 7.4.4.8.2 Abstract equation
+
+$$\{SMS | SDS\} \text{ consumed confirmation failure ratio}[\%] = \frac{\text{non - confirmed}\{SMS | SDS\} \text{ consumptions}}{\text{all}\{SMS | SDS\} \text{ service attempts}} \times 100$$
+
+#### 7.4.4.8.3 Trigger points
+
+GSM/UMTS:
+
+| Event from abstract equation | Trigger point from user's point of view | Technical description/ protocol part |
+|-------------------------------|---------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| SMS service attempt | Start: Push the send button (initiate sending an SMS). | Start:
Layer 3: The first "Access request" is sent by the originating UE (Figure 7-59, most upper signalling point).
Detailed: CM service request is sent by the originating UE.
AT: The "AT+CMGS=" or "AT+CMGS=" (parameter depends on the "AT+CMGF" setting, PDU or text mode) command is sent by the originating TE. |
+| Confirmed SMS consumption | Stop: Receive the confirmation at the initiating party that the message is received at the terminating party. | Stop:
To be defined. |
+| Non-confirmed SMS consumption | Stop trigger point not reached. | |
+
+TETRA:
+
+| Event from abstract equation | Trigger point from user's point of view | Technical description/ protocol part |
+|------------------------------|--------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| SMS service attempt | Start: Push the send button (initiate sending an SDS). | Start:
Layer 3 (CMCE): The first "U-SDS-DATA" message carrying the "SDS-TRANSFER" message with delivery report request "Message consumed report requested" or "Message received and consumed report requested" is sent by the originating UE.
AT: The "AT+CMGS=", " " command is sent by the originating TE. |
+
+| Event from abstract equation | Trigger point from user's point of view | Technical description/ protocol part |
+|-------------------------------|--------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| Confirmed SMS consumption | Stop: Receive the confirmation at the initiating party that the message is consumed. | Stop:
Layer 3 (CMCE): The "D-SDS-DATA" message carrying the "SDS-REPORT" with delivery status "SDS consumed by destination" is received by the originating UE.
AT: To be defined. |
+| Non-confirmed SMS consumption | Stop trigger point not reached. | |
+
+#### 7.4.4.9 {SMS | SDS} consumed confirmation time [s]
+
+##### 7.4.4.9.1 Abstract definition
+
+The {SMS | SDS} consumed confirmation time is the time period between sending a short message to the network and receiving the consumed confirmation from the network.
+
+##### 7.4.4.9.2 Abstract equation
+
+$$\{ \text{SMS} | \text{SDS} \} \text{ consumed confirmation time [s]} = (t_{A, \text{consumed confirmation}} - t_{A, \text{send}}) [s]$$
+
+##### 7.4.4.9.3 Trigger points
+
+GSM/UMTS:
+
+| Event from abstract equation | Trigger point from user's point of view | Technical description/ protocol part |
+|---------------------------------------|---------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| $t_{A, \text{send}}$ | Start: Push the send button (initiate sending an SMS). | Start:
Layer 3: The first "Access request" is sent by the originating UE (Figure 7-59, most upper signalling point).
Detailed: CM service request is sent by the originating UE.
AT: The "AT+CMGS=" or "AT+CMGS=" (parameter depends on the "AT+CMGF" setting, PDU or text mode) command is sent by the originating TE. |
+| $t_{A, \text{consumed confirmation}}$ | Stop: Receive the confirmation at the initiating party that the message is consumed at the terminating party. | Stop:
To be defined. |
+
+## TETRA:
+
+| Event from abstract equation | Trigger point from user's point of view | Technical description/ protocol part |
+|--------------------------------|---------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| $t_{A,send}$ | Start: Push the send button (initiate sending an SDS). | Start:
Layer 3 (CMCE): The first "U-SDS-DATA" message carrying the "SDS-TRANSFER" message with delivery report request "Message consumed report requested" or "Message received and consumed report requested" is sent by the originating UE.
AT: The "AT+CMGS="", " command is sent by the originating TE. |
+| $t_{A,consumed\ confirmation}$ | Stop: Receive the confirmation at the initiating party that the message is consumed at the terminating party. | Stop:
Layer 3 (CMCE): The "D-SDS-DATA" message carrying the "SDS-REPORT" with delivery status "SDS consumed by destination" is received by the originating UE.
AT: To be defined. |
+
+## 8 Typical procedures for quality of service measurement equipment
+
+### 8.1 Aim of measurement
+
+The aim of the measurements described in this Recommendation is to assess the network under test for its quality of service (QoS) parameters as defined in clause 7. This is, to determine the network quality for the respective transactions from the user's view.
+
+### 8.2 Classification of services
+
+#### 8.2.1 Classification guidelines
+
+For the purpose of this Recommendation, services are classified using what is considered to be their dominating property. The first distinction is made between direct and store-and-forward services:
+
+- Direct-transaction services are services where there is – in the user's perception – a direct end-to-end connection.
+- Store-and-forward services are services where content is stored in the network and delivered to the recipient at a later point in time.
+
+As a technically usable differentiation, a service is considered to be direct if it is possible to decide on end-to-end content transfer success from the initiating party (A-party) of the connection within the scope of the transaction itself.
+
+NOTE – e-mail is a special case since it has both aspects of direct and of store-and-forward services. More information on store-and-forward services measurement is provided in clause 8.5.
+
+## 8.2.2 General structure of service descriptions
+
+In the following, each service family description will contain the following structural elements:
+
+- A general part defining:
+ - the basic transaction definition and if applicable, transaction types;
+ - a description of the transaction phase combined with a table of parameters governing transaction behaviour in this phase;
+ - a description of all possible outcomes of a single transaction;
+ - a description of content quality measurement definitions (if applicable).
+- In case there are service-dependent differences, a service-dependent part having the same structure as above.
+
+## 8.3 General aspects for all types of services
+
+### 8.3.1 Set-up and control
+
+Measurements should be conducted in a way that user behaviour is realistically modelled. Parameters and settings which have substantial influence on results need to be under control of the measurement equipment.
+
+The test case design (configuration and user profile) – to the degree necessary to fully reproduce the test – shall be part of the measurement documentation.
+
+It is assumed that for all types of services under test, a test case consists of a number of single identical transactions. The measurement equipment and control must ensure that the starting conditions are the same for each transaction. This includes, among other things, that pause times are sufficiently long that the equipment is in a stable (idle) state again. The parameter "guard time" sets a minimum value for the pause between transactions.
+
+It is assumed that all QoS-relevant transaction parameters are recorded for proper post-processing and are kept constant during measurements. If a measurement contains more than one parameter set, evaluation shall be made for each parameter set separately.
+
+### 8.3.2 Phase and result classification
+
+In order to ensure common wording, the following clause defines terms and definitions for service measurements.
+
+It is assumed that each transaction can be described at least by one seamless sequence of phases. There may exist several angles of view (AOV), each leading to a different phase description.
+
+Example: Internet services (as described by its QoS parameters defined in clause 7.1.2 and method A and method B). AOV differ here by different assumptions on start of service usage. Each AOV, however, is a consistent description by seamlessly connected phases.
+
+Phases may be further described having sub-phases.
+
+Pauses between transactions are not explicitly mentioned, but are relevant with respect to parameter reporting. Typically, there is a minimum pause (guard time) ensuring that the system under test is in a stable starting condition for the next test. Values are technology-dependent.
+
+#### 8.3.2.1 Phase and result classification for direct services
+
+A direct transaction consists of two top-level phases: service access and service usage.
+
+Table 8-1 shows general phase definitions for direct services while Table 8-2 shows the general result classification for direct services.
+
+**Table 8-1 – General phase definitions for direct services**
+
+| Phase | Sub-phase | Definition |
+|----------------|-------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| Service access | | All steps leading to the technical ability to perform actual user-perspective content transport between A- and B-party. Service access may consist of different sub-phases, e.g., Network access, IP service access and Internet access. The availability of these sub-phases actually depends on the particular service. |
+| | Network access | Basic access to the network under test. Successful network access is assumed when the UE is able to do as much basic communication with the network as is necessary to initiate the next phase in the service access procedure. |
+| | IP service access | Basic access to the generic packet-data transfer capabilities the particular service is based upon. |
+| | Internet access | Basic access to those Internet services the service is meant to provide. |
+| Service usage | | Content transfer between the A- party and the B-party. |
+
+A direct transaction may have one of the following overall results.
+
+**Table 8-2 – General result classification for direct services**
+
+| Result | Definition |
+|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| Failed | Phase of service usage not reached.
Successful or failed service access may be broken down into diagnostic sub-categories. The general name-forming rule is: result.
Example: Network access failed |
+| Completed | Data-transfer transactions: All content intended to be transferred has been successfully transferred.
Conversational transactions: The intended transaction duration has been reached. |
+| Dropped/Cut-off | Service usage was ended before completion. |
+| NOTE – If a transaction, while being in the service usage phase, is stopped due to some timeout or due to other criteria by the measurement system, e.g., to enhance test rate, this shall be treated as a dropped transaction. This behaviour has to be recorded by the measurement system. | |
+
+### 8.3.2.2 Phase and result classification for store-and-forward services
+
+A store-and-forward transaction consists of two top-level phases: content sending and content delivery.
+
+Table 8-3 provides general phase definitions for store-and-forward services and Table 8-4 provides the general result classification for store-and-forward services.
+
+**Table 8-3 – General phase definitions for store-and-forward services**
+
+| Phase | Sub-phase | Definition |
+|------------------|-------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| Content sending | | All steps required to transfer the content to the network, up to the point where the network is able to start delivery. This phase is completed when there is nothing more the A-party can/needs to do to transfer the content to the B-party. It is assumed that the A-party gets information sufficient to judge if sending has been successful or not. |
+| Content delivery | | All steps to transfer the content from the network to the B-party. Delivery may consist of two main sub-phases: notification and retrieval . |
+| | Notification (optional) | Information sent to the B-party that content is ready for transfer. |
+| | Retrieval/Delivery | Transport of content from the network to the B-party. |
+
+A store-and-forward transaction may have one of the following overall results.
+
+**Table 8-4 – General result classification for store-and-forward services**
+
+| Result | Sub-category | Definition |
+|-----------|--------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| Completed | | Content was successfully transferred from the A- party to the B-party. |
+| Failed | | Content was not successfully transferred from the A-party to the B-party. Depending on the particular services and the available information, there may be a number of possible sub-categories for this result. |
+| | Undelivered | Content was successfully sent to the network, but was never delivered or retrieved. |
+| | Send failed | Content was not successfully delivered to the network. |
+| | Lost | Content was successfully sent to the network, but notification was never received by the B-party. Diagnostic sub-category in case notification can/shall be technically identified within the delivery process. |
+| | Timeout | The transaction was completed, but the content delivery time was above a given threshold. |
+
+## 8.4 Telephony measurements
+
+This clause deals with telephony services. In general, the term "content" will be used throughout this clause for the information flow exchanged between participating users during a call. Depending on the type of service, content can be audio or audio and video.
+
+### 8.4.1 General aspects
+
+#### 8.4.1.1 Transaction definition and transaction types
+
+The basic transaction for telephony testing is equivalent to a single call to a counterpart extension.
+
+Table 8-5 provides an overview of parameters for telephony measurements.
+
+#### 8.4.1.2 Parameter overview
+
+**Table 8-5 – Parameter overview for telephony measurements**
+
+| Phase | Parameters |
+|-----------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| Service access | Call counterpart. This includes the type of equipment (dedicated unit, unique identifier (e.g., called party number), automatic answer with taped message, etc.). |
+| | Call type. |
+| | Timeout value |
+| Service usage | Call duration. |
+| | Content flow direction: This is an inner parameter for a transaction. Basically, all combinations of uplink/downlink dynamics are possible: - – Uplink only;
- – Downlink only;
- – Conversational (alternating uplink and downlink). This is the recommended standard testing mode. Other testing modes are considered to be used only for special purposes;
- – "Duplex" (uplink and downlink flow simultaneously).
|
+| | Codec settings. |
+| | Algorithm and scale used for speech quality evaluation. |
+| | |
+| Call clear-down | Guard time. |
+| Pause | Pause duration. For current UEs, a pause time of at least 15 seconds (guard time) is recommended.
However, this duration may be adjusted to local conditions or special testing goals, but this must be reported.
If the pause duration is too short, side effects may occur, resulting in all kinds of transient effects and distortions in measurement data. It should be made certain that all the QoS parameters to be measured are not affected by the pause time.
|
+
+The last transaction within a measurement sequence does not require a pause.
+
+#### 8.4.1.3 Additional transaction result definitions
+
+For call set-up assessment beyond QoS data acquisition, typically a state model driven by suitable trigger-events information combined with information from the call control engine is being used. This state model may also be used to determine timing information for each phase.
+
+Service usability, i.e., presence of a usable two-way connection, shall be verified by a procedure based on content test transmissions within a given time window. If within this time window no connection can be verified, the set-up attempt shall be considered as failed and the call attempt be terminated.
+
+A call is active only as long as both sides consider it to be active. A call is therefore considered to be dropped if either side detects a dropped call.
+
+Above definitions lead to the following decision tree for the outcome of a call (Figure 8-1 includes the end-of-call cases).
+
+
+
+```
+
+graph TD
+ Start(( )) --> CallAttempt[Call attempt]
+ CallAttempt --> Decision1{ }
+ Decision1 --> AccessFailure[Access failure]
+ Decision1 --> Success1[Successful network access]
+ Success1 --> Decision2{ }
+ Decision2 --> SetupFailure[Setup failure]
+ Decision2 --> Success2[Setup success]
+ Success2 --> Decision3{ }
+ Decision3 --> DroppedCall[Dropped call]
+ Decision3 --> NotDropped[Call not dropped]
+ NotDropped --> CompletedCall[Completed call]
+
+```
+
+E.804(14)\_F8-1
+
+Flowchart of Telephony measurement outcome
+
+**Figure 8-1 – Telephony measurement outcome**
+
+#### 8.4.1.4 Content quality
+
+For content quality assessment, data is generated at the receiving end. For downlink content, data storage is therefore straightforward; quality-assessment data is simply included with other data items making up the result file. For uplink content, at some point in time results from the involved B-parties have to be combined with the data from the originating A-party.
+
+For assessing content quality of complete transmitted speech samples, at least the following methods are possible:
+
+- Real time assessment (streaming mode), where the quality assessment algorithm continuously outputs the defined quality measures.
+- "Offline" assessment, where content is first recorded in some way and later being processed.
+
+Data processing must make sure that only such content quality data is used which lies inside the "connection active" time window and is in line with one of the defined content quality parameters.
+
+#### 8.4.1.5 Verification of usable two-way connection
+
+Only calls with a valid two-way end-to-end information connection shall be considered for content quality assessment (valid calls).
+
+A non-valid call shall be treated like a dropped call, with a modifier indicating this particular cause.
+
+### 8.4.2 Speech telephony
+
+#### 8.4.2.1 Transaction definition and transaction types for speech telephony
+
+See clause 8.4.1.
+
+#### 8.4.2.2 Parameter overview for speech telephony
+
+See clause 8.4.1.
+
+#### 8.4.2.3 Additional transaction results for speech telephony
+
+See clause 8.4.1.
+
+#### 8.4.2.4 Content quality for speech telephony
+
+See clause 8.4.1.
+
+#### 8.4.2.5 Verification of usable two-way connection for speech telephony
+
+This shall be verified by a procedure based on audio test transmissions within a given time window. If within this time window no audio connection can be verified, the set-up attempt shall be considered to be failed and the call attempt be terminated.
+
+NOTE – To make sure an audio connection is valid, it is assumed that an appropriate kind of data analysis on audio flow is performed (see [ITU-T P.56]).
+
+### 8.4.3 Video telephony
+
+#### 8.4.3.1 Transaction definition and transaction types for video telephony
+
+The basic transaction for video telephony testing is equivalent to a single call to a counterpart extension.
+
+Due to existing usage and hardware, typical video calls will be between UEs, so no further call types are distinguished.
+
+Unlike other services, it is currently assumed that video-call testing will require a high degree of abstraction in the sense that testing-system architectures may differ quite thoroughly from those found in the "real connections". Presently, there are no known terminal-based protocol stacks delivering data necessary for testing or QoS assessment. Therefore, it is assumed that PC-based implementations of video protocol stacks have to be used, where the UE serves only as the modem part of the connection.
+
+It is further assumed that typical tests will use data which serve as load and carry diagnostic data at the same time.
+
+#### 8.4.3.2 Parameter overview for video telephony
+
+Table 8-6 provides parameter overview for video telephony measurements.
+
+NOTE – Content flow will typically be governed by the video protocol and is assumed to involve simultaneous data flow in both directions.
+
+**Table 8-6 – Parameter overview for video telephony measurements**
+
+| Phase | Parameters |
+|-----------------|-----------------------------------------------------------------------------------------------------------------------------------------|
+| Service access | Connection type (CSD bearer type). |
+| | Video-telephony protocol used. |
+| | Call counterpart technology and architecture. This includes the type of test being made (e.g., "transparent" test of modem connection). |
+| | Timeout value. |
+| Service usage | Call duration. |
+| Call clear-down | Guard time. |
+| Pause | Pause duration. |
+
+The last transaction within a measurement sequence does not require a pause.
+
+#### 8.4.3.3 Additional transaction result definitions for video telephony
+
+It is assumed that the A and B party video-telephony protocol stacks are compatible, and given a usable end-to-end connection of proper type, a video-telephony connection can be established. It is assumed that if the network cannot provide a connection with the required bearer properties, this will either result in failure to provide a usable end-to-end connection or the video-telephony protocol stack will detect this by indicating connection failure.
+
+It is conceivable that the network provides the wrong bearer type. Due to limited "decision power" determining the usability of an end-to-end connection, this could go undetected at first, but leads to quality deficiencies due to inappropriate bearer properties.
+
+#### **8.4.3.4 Content quality for video telephony**
+
+Currently no stable specification for video quality exists. It is assumed that the audio part of the connection will be tested essentially the same way as for speech telephony.
+
+#### **8.4.3.5 Verification of usable two-way connection for video telephony**
+
+It is assumed that the video telephony protocol being used implicitly checks for useful connection.
+
+#### **8.4.3.6 Call set-up considerations for MTSI calls**
+
+When MTSI calls are set up, any network address translation (NAT) devices present in the call path need to be opened. This can be done automatically by the network for managed NATs, or handled by the MTSI clients for unmanaged NATs. The call set-up, as seen by the calling party, is assumed to be done when the first valid media packet is received from the called party. This should be interpreted as the first packet containing the negotiated media payload type.
+
+The RTP standard has a number of fixed media payload types, but MTSI uses dynamic payload type assignments, which means that the media payload types are allocated from the range 96 to 127. The relation between the payload type and the actual codec and codec format used is then defined in the SDP during the call set-up negotiation.
+
+Thus the client needs to use the information in the SDP and the outcome of the negotiation, to identify which payload type is finally agreed, and use the first reception of that payload type as the end of the call set-up phase.
+
+### **8.4.4 Group call**
+
+#### **8.4.4.1 Transaction definition and transaction types for group calls**
+
+The basic transaction for group call testing is an extension to an individual telephony speech call with two parties involved (A-party and B-party). One Test UE (A-party) will call $n$ counterparts ( $B_i$ -parties) having $1 \leq i \leq n$ . The $n$ counterparts are typically Test UEs.
+
+Type is group call (GC) and it will not be distinguished between mobile originating (MO) and mobile termination (MT).
+
+In principle, two options for a typical measurement scenario are available. Either all group members may be connected to the same common measurement system at one location (not limiting that the measurement system might be in mobility) or each group member is connected to a separate measurement system and these might be distributed at various locations (again not limiting that all or some of the measurement systems might be in mobility).
+
+#### **8.4.4.2 Parameter overview for group calls**
+
+Table 8-7 provides a parameter overview for group call measurements.
+
+**Table 8-7 – Parameter overview for group call measurements**
+
+| Phase | Parameters |
+|-----------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| Service access | Call counterparts (group to call). |
+| | Call type is group call (GC). |
+| | Timeout for failed-call condition. |
+| Service usage | Call duration. |
+| | Content flow direction: Typically, only one party can talk at one point in time. A pattern may be specified, which describes, at what point in time a certain party has to talk to the others. |
+| Call clear-down | Guard time. |
+| Pause | Pause duration. Depending on the used radio access technology, the duration of required pauses may differ significantly.
However, this duration may be adjusted to local conditions or special testing goals, but this must be reported.
If the pause duration is too short, side effects may occur, resulting in all kinds of transient effects and distortions in measurement data. It should be made certain that all the QoS parameters to be measured are not affected by the pause time. |
+
+The last transaction within a test sequence does not require a pause.
+
+#### 8.4.4.3 Additional transaction result definitions for group calls
+
+It is assumed that all members of the target group are known by the measurement system(s). All group members are controlled and called exclusively by the measurement system(s).
+
+For quality measurements, the following rules apply: As a first measurement option, all parties may talk the same amount of time. The sequence of the talking party shall be A-Party, B1-Party, B2-Party, ..., Bn-Party. A group call always ends after the Bn-Party has finished its last talking period. In this case, $N \times (N+1)$ quality values are measured per group call. A quality value per group call should apply statistical means (e.g., a simple approach would be the arithmetic average) with respect to aggregation and is for further study.
+
+As a second measurement option, only the initiating A-Party will generate a talk burst and the group call is ended after the speech phase of the A-Party. In this case, N quality values are measured per group call.
+
+In both cases, the roles of the participating group members might change, e.g., a former B-Party can cyclically take the role of the initiating A-party whereas the former A-party gets the role of a B-Party.
+
+If a Bi-Party is disconnected during the group call, the quality values of this Bi-Party will be excluded from the overall quality value.
+
+#### 8.4.4.4 Content quality for group calls
+
+See clause 8.4.1.
+
+#### 8.4.4.5 Verification of usable connection for group calls
+
+The usability of the connection will be tested implicitly by the content quality algorithm.
+
+## 8.5 Store-and-forward services measurement
+
+### 8.5.1 General aspects
+
+The basic transaction for store-and-forward services testing is equivalent to transmission of a single unit of content (which may, however, contain several parts such as audio, video and text) between two parties.
+
+Typically, content is, in the end-to-end perspective, being sent either from some fixed-network source to a UE (mobile termination, MT) or from one UE to another (mobile originated to mobile, MOM). However, MOM testing with a two-UE set-up may cause additional uncertainty due to coverage and signalling effects in the destination UE.
+
+Basically, there are two recommended methods of testing:
+
+- a) using a destination UE in a fixed location with virtually 100 per cent transfer probability. Part of the measurement procedure should be cyclical reference checks to assure reception quality respectively to create baseline data; or
+- b) using a fixed-network destination such as a large account server which is accessed by suitable means from a fixed-network location.
+
+In all cases, testing methodology requires that measurement data are being collected in at least two locations and need to be integrated before the final QoS evaluation can be made.
+
+For practical reasons, measurements are typically limited to a certain time window. Technically it is possible that content being sent in previous test session arrives at the system after the session has been finalized. Technical provisions are needed to ensure proper handling of such content, either by ignoring it or by correctly assigning it in post-processing.
+
+Content used by the measurement system shall carry appropriate signature to identify it as belonging to the measurement, to identify the test session within it was sent, and to enable unambiguous correlation between sent and received content.
+
+#### 8.5.1.1 Transaction phase and parameter overview
+
+Table 8-8 provides transaction phase and parameter overview for store-and-forward services.
+
+**Table 8-8 – Transaction phase and parameter overview for store-and-forward services**
+
+| Phase | Parameters |
+|--------------|-----------------------------|
+| Sending | Destination equipment type. |
+| Notification | Timeout for notification. |
+| Retrieval | |
+| Pause | Pause duration. |
+
+#### 8.5.1.2 Additional transaction result definitions
+
+The nature of store-and-forward services implies that loss rates need precise definition. From the user's point of view, there is expectation that content is delivered within the maximum expected delivery time (MEDT). Even if technically an outstanding content item may be delivered later, it should be considered lost anyway. This leads, in turn, to the problem of over-aged content items which may disturb subsequent measurements. Suitable means of eliminating such effects should be taken, e.g., if the service allows for it, setting a reasonable lifetime for the content item, and using unique sequence numbering in a way to prevent aforementioned effects.
+
+Post-processing must ensure that, if the measurement system is shut down before a given maximum waiting time has expired, all pending units of content, i.e., those with an age less than MEDT are
+
+completely removed from QoS evaluation since there is no way to predict if these elements will arrive within MEDT or not.
+
+The influence of over-aged content should be reduced to a minimum by using a selective retrieval. Only the content delivered within the MEDT should be downloaded when possible.
+
+### **8.5.1.3 Content quality for store-and-forward services**
+
+Definitions of these service types are given in the respective service-related clauses.
+
+## **8.5.2 SMS measurements**
+
+### **8.5.2.1 General aspects of SMS measurements**
+
+There are two modes for SMS transmission:
+
+- text mode;
+- PDU mode.
+
+Basically, these modes should be equivalent including options available. In practice, mode support is mobile-dependent.
+
+For each SMS, a "lifetime" can be set after which a SMS is deleted in the short message service centre (SMSC). While in PDU mode, this parameter is part of the parameter structure, in text mode it is set in the mobile device, which may not be supported by all mobile device types. This leads to the recommendation that PDU mode should be used.
+
+### **8.5.2.2 Transaction definitions and transaction types for SMS**
+
+The basic transaction for SMS testing is equivalent to transmission of a single SMS.
+
+Typically, user SMS are being sent either from some fixed-network source to a mobile (SMS mobile termination, SMS-MT), or from one mobile to another (SMS mobile originating, SMS-MO, with regard to mobile-based testing). However, uplink SMS testing with a two-mobile set-up may cause additional uncertainty due to coverage and signalling effects at the destination UE. Basically, there are two recommended methods of testing:
+
+- using a destination mobile in a fixed location with virtually 100 per cent transfer probability. Part of the measurement procedure should be cyclical reference checks to assure reception quality respectively to create baseline data; or
+- using a fixed-network destination such as a large account SMS server which is accessed by suitable means from a fixed-network location.
+
+In all cases, testing methodology requires that measurement data be collected in at least two locations and be integrated before the final QoS evaluation can be made.
+
+To ensure comparability and statistical validity of transactions, the following outer conditions need to be constant throughout a test case:
+
+- SMS mode.
+- Call counterpart.
+- SMSC being used.
+- Call timing including behaviour in case of delivery failure to the network.
+
+For reasons of comparability in density, it is required that all SMS sending attempts which form part of the statistics must follow a constant time pattern. Additional SMS sending attempts, e.g., in the case of failure of delivery to the network must not affect the pattern of the statistically relevant call attempts.
+
+SMS being sent from either side should contain information enabling validity and integrity checking during evaluation. The recommended minimum set includes:
+
+- signature assuring that only test system SMS are being considered;
+- sequence number for SMS delivery timing.
+
+If required, a test SMS may contain additional data (padding) to obtain SMS with a length assumed to be typical for a particular network.
+
+If required, more powerful means of ensuring that only SMS generated by the test system can be added. For example, the SMS may contain a code which is created from checksum content and a seed value delivered by the receiving side. This ensures that even if a static signature is duplicated by operational errors or malfunction of other test systems, the system will be robust against it.
+
+Furthermore, for the general design of SMS testing, communication between the mobile testing system and its stationary counterpart is required to ensure certain starting conditions:
+
+- Prior to actual testing, the SMS storage of the UE should be cleared to exclude SMS delivery failure due to memory shortage.
+- The stationary side should start sending SMS only after a "go" from the mobile side, ensuring that no transient effects occur.
+
+### 8.5.2.3 Testing mode for SMS-MT
+
+For this type of testing, SMS are being generated at a constant rate by the stationary side.
+
+All SMS received which have not been generated by the stationary side shall be ignored for quality assessment.
+
+For reception behaviour, two modes exist; it is the mobile-dependent modes which are supported.
+
+- a) On SMS reception, the mobile device generates an indication message (CMTI message) on its data connection to the personal computer (PC). The actual SMS can then be requested by a command.
+- b) The mobile device outputs the SMS directly on its data connection to the PC.
+
+From the control and trigger point accuracy considerations, mode a) is preferred.
+
+### 8.5.2.4 Testing mode for SMS-MO
+
+For this type of testing, SMS are being generated at a constant rate by the mobile side, plus eventual extra SMS in case of failure of delivery to the network.
+
+### 8.5.2.5 Transaction phase and parameter overview for SMS
+
+Table 8-9 provides transaction phase and parameter overview for SMS.
+
+**Table 8-9 – Transaction phase and parameter overview for SMS**
+
+| Phase | Parameters |
+|--------------|------------------------------------------------------------------------------------------------------------------|
+| Sending | Type and destination equipment type (depends on type, e.g., to mobile device, to large account SMS server, etc.) |
+| | SMSC used |
+| | Content size |
+| Notification | Timeout for notification |
+| | Notification mode for the incoming SMS |
+| Retrieval | |
+| Pause | Pause duration. For current GSM equipment, a pause time of at least 15 s (guard time) is recommended |
+
+#### **8.5.2.6 Possible transaction results for SMS**
+
+See clause 8.5.1.
+
+#### **8.5.2.7 Content quality for SMS**
+
+It is assumed that the SMS service protect its payload sufficiently so it can be assumed that no distortions in content will occur.
+
+### **8.5.3 MMS**
+
+#### **8.5.3.1 General aspects of MMS measurements**
+
+With respect to general aspects of store-and-forward systems in MMS testing special care has to be taken for proper handling of the incoming notification SMS not belonging to the active session. They shall be treated as follows:
+
+- Incoming SMS not being a valid push SMS shall be ignored.
+- Incoming valid push SMS identified as being sent by the testing system but not belonging to the current test session may be ignored (see general remarks).
+- For all other push SMS, it is optional to download the MMS and to check if it belongs to the measurement.
+
+NOTE – If the network under test modifies the push SMS content in a way that it cannot be detected if the push SMS belongs to the measurement, ignoring the push SMS has the risk that a successful MMS from the user's perspective is reported as unsuccessful by the measurement system.
+
+#### **8.5.3.2 Transaction definitions and transaction types for MMS**
+
+The basic transaction for MMS testing is equivalent to transmission of a single MMS, which may however contain several parts such as picture or video, audio and text.
+
+Typically, user MMS are being sent either from a mobile device to another mobile device (MOM or MTM depending on measurement set-up), or from a mobile device to a fixed-network location (MOF). In the case of MMS the destination is typically one or more e-mail addresses.
+
+In case a destination mobile device is classified by the network as not capable of MMS reception, a "legacy SMS" is transmitted instead. This SMS will contain information on how MMS content can be downloaded via Internet. The format of such legacy SMS is not standardized.
+
+In mobile-to-mobile scenarios there is always the risk of ambiguity with respect to end-to-end quality assessment. In the case of store-and-forward measurements, this is less critical due to the definition of QoS parameters which allows distinguishing the uplink and the downlink components. Nevertheless, QoS parameters expression end-to-end performance will be affected. Therefore, two methods of testing are recommended:
+
+- a) using a B-party mobile in a fixed location with very good network coverage. Part of the measurement procedure should be appropriate cyclical reference checks to obtain baseline data; or
+- b) using an e-mail destination.
+
+To ensure comparability and statistical validity of transactions, the following outer conditions need to be constant throughout a test case:
+
+- MMS content;
+- call counterpart;
+- MMS proxy being used;
+- send timing including behaviour in case of delivery failure to the network.
+
+For reasons of comparability in density, it is required that all MMS sending attempts which form part of the statistics must follow a constant time pattern. Additional MMS sending attempts, e.g., in the case of failure of delivery to the network must not affect the pattern of the statistically relevant call attempts.
+
+MMS being sent from either side should contain information enabling validity and integrity checking during the evaluation. The recommended minimum set includes:
+
+- signature assuring that only test system MMS are being considered;
+- sequence number for MMS delivery timing.
+
+If required, a test MMS may contain additional data (padding) to obtain content with structure and size assumed to be typical for QoS measurement purposes.
+
+Further for general design of MMS testing, communication between the mobile testing system and its stationary counterpart is required to ensure that prior to actual testing, the MMS and SMS storage of the UE are cleared to exclude failures due to memory shortage or other limitation.
+
+### 8.5.3.3 Transaction phase and parameter overview for MMS
+
+Unlike SMS, posting and retrieving MMS is handled via a network service (WAP) rather than embedded in low-level functionality of the network. Also, uploading or downloading MMS is a process which takes considerable time. Therefore, the simple phase scheme – and, consequently, the possible-result definitions, have been extended by inserting subphases.
+
+Table 8-10 shows transaction phase and parameter overview for MMS.
+
+**Table 8-10 – Transaction phase and parameter overview for MMS**
+
+| Phase | Subphase | Parameters |
+|--------------|--------------------|-----------------------------------------------------------------------------------------------|
+| Sending | Network access | Equipment types and capabilities |
+| | MMS gateway access | MMS gateway address (proxy used) |
+| | Upload | Content composition
Content size
User agent string (identification of the receiving UE) |
+| Notification | | Timeout for notification |
+| Retrieval | Network access | Equipment type and capabilities |
+| | MMS gateway access | MMS gateway address (proxy used) |
+| | Download | |
+| Pause | | Time between MMS postings |
+
+### 8.5.3.4 Additional transaction result definitions for MMS
+
+The following definitions are based on the assumption that, from technical feedback during the service usage, the user notices (and cares about) different ways a MMS transaction can fail. The following definitions are to be seen in conjunction with other similar result definitions:
+
+- Service access failed: The MMS transaction could not be started due to inability to access the network or the service.
+- Dropped while posting: Content could not be uploaded successfully.
+- Lost: The content package was successfully uploaded, but the recipient did not get notification within the MEDT.
+- Dropped while retrieving: The recipient received a notification, but was unable to retrieve the MMS.
+
+NOTE – Unlike for the upload, this includes network or service access failure.
+
+- Completed: Content has been successfully delivered to the B-party (which includes completeness of the package).
+
+### 8.5.3.5 Content quality for MMS
+
+In MMS transmission, transcoding may be used by the MMS infrastructure to adapt content from the format of the sending UE to a format supported by the receiving UE. Due to the fact that transcoding is integral part of MMS which is an explicit change of content, no direct comparison of content sent and received is possible.
+
+It is currently assumed that correct transcoding is validated by means other than QoS measurements. Therefore, it is not subject of this Recommendation.
+
+## 8.5.4 E-mail
+
+### 8.5.4.1 Transaction definitions for e-mail
+
+E-mails are sent from one e-mail client to another one via one or more e-mail servers. Both clients can independently use a wireless or a wired connection to their respective server.
+
+Figure 8-2 depicts transaction definitions for e-mail.
+
+
+
+The diagram illustrates the transaction definitions for e-mail. It shows a sequence of components: Client A (represented by a mobile phone icon), Server A (a server rack icon), Server B (another server rack icon), and Client B (a laptop icon). Red arrows indicate the flow of data from Client A to Server A, then to Server B, and finally to Client B. Below this flow, three horizontal lines with brackets define parameter sets: 'Upload parameters' are defined between Client A and Server A; 'Download parameters' are defined between Server B and Client B; and 'End-to-end parameters' are defined between Client A and Client B.
+
+Diagram illustrating transaction definitions for e-mail. It shows a flow from Client A (mobile phone) to Server A, then to Server B, and finally to Client B (laptop). Red arrows indicate the direction of data flow. Below the flow, three horizontal lines with brackets define parameter sets: 'Upload parameters' between Client A and Server A, 'Download parameters' between Server B and Client B, and 'End-to-end parameters' spanning from Client A to Client B.
+
+Figure 8-2 – Transaction definitions for e-mail
+
+### 8.5.4.1.1 Protocols
+
+The following protocols apply to e-mail transactions:
+
+- For upload:
+ - Simple mail transfer protocol (SMTP) [IETF RFC 2821].
+- For download:
+ - Internet message access protocol, version 4 (IMAP4), see [b-IETF RFC 3501];
+ - Post office protocol, version 3 (POP3) [IETF RFC 1939].
+
+NOTE – The POP3 protocol does not follow the store-and-forward approach because it is not capable to push (i.e., forward) e-mails to the receiver side. When using the POP3 protocol, the client has to poll the e-mail server for new e-mails.
+
+### 8.5.4.1.2 Reference content
+
+An e-mail consists of a header and a body. The body may contain text (e.g., plain text or HTML) or attachments (e.g., archives, music, documents, etc.).
+
+For the download tests, it must be assured that the required reference content is present on the server. For the upload tests, it must be assured that successful upload is not prevented due to storage size limitations.
+
+In addition, the measurements should not be influenced by additional (unwanted) or old e-mails. Appropriate measures should be taken (e.g., junk filter or clean-up of e-mail accounts).
+
+Each e-mail uploaded shall have a unique identifier inside the e-mail header for unambiguous identification. Each e-mail downloaded shall have a unique identifier inside the e-mail header and the e-mail body for unambiguous identification. This unique identifier can be used for the aggregation of test results using the same set-up (e.g., e-mails of the same size).
+
+Each e-mail uploaded shall have a unique test probe identifier inside the e-mail header to identify the uploading system. This is needed when different systems are uploading e-mails to a commonly used receiving account.
+
+Refer to clause 10 for more details on the reference content to be used for e-mail testing.
+
+#### **8.5.4.1.3 Content integrity**
+
+After each e-mail transfer, it should be checked if the transferred reference content is received completely and is identical to the original data (including attachments).
+
+For e-mail upload, additional procedures are necessary to guarantee the integrity of the transferred content.
+
+The measured values of all QoS parameters, except for the access related QoS parameters, depend on the e-mail content used. Therefore, it is only allowed to aggregate test results which used the same e-mail content set-up, except for the access-related QoS parameters.
+
+#### **8.5.4.1.4 Push functionality**
+
+Both the client and the server shall support push functionality ("idle" feature) for tests using the IMAP4 protocol [IETF RFC 2177].
+
+#### **8.5.4.1.5 Header only download**
+
+The e-mail client used shall support the feature of downloading only the header of an e-mail contained within the inbox.
+
+#### **8.5.4.1.6 Timeouts**
+
+Every QoS parameter has its own configurable timeout setting which depends on the reference content and the technology used.
+
+Refer to clause 10 for timeout settings.
+
+#### **8.5.4.1.7 General requirements and limitations**
+
+The used e-mail client should be comparable to popular e-mail clients used by users with respect to the behaviour and the performance. The performance of the used server should be comparable to commercially used systems.
+
+The following information shall be logged:
+
+- Type of e-mail client used (including version number, build, maximum number of parallel sockets).
+- Authentication mode used.
+- In case that a commercial client is used for testing not all of the pauses defined in clause 8.5.4.2 may be user configurable.
+
+#### 8.5.4.2 Transaction scenarios for e-mail
+
+The following transaction scenarios are possible to test the service from an end user perspective:
+
+- 1) Upload scenario.
+- 2) Download scenario.
+- 3) End-to-end scenario.
+
+The first scenario only considers uploading an e-mail, while the second scenario only examines the download path. The third scenario tests the complete service chain, from the sending an e-mail by the A-party to the reception of the e-mail by the B-party.
+
+##### 8.5.4.2.1 Upload scenario
+
+The upload test cycle is defined as follows:
+
+- 1) Connect to the mobile network (set up IP connectivity);
+- 2) Configurable pause (default 15 s);
+- 3) Login client A to server A;
+- 4) Configurable pause (default 15 s);
+- 5) Attempt to upload up to, e.g., ten e-mails to server A, with a configurable pause (default 15 s) after each upload;
+- 6) Disconnect from the mobile network;
+- 7) Configurable pause (default 15 s) before starting the next test sequence.
+
+Figure 8-3 shows message flow for e-mail upload scenario.
+
+
+
+```
+sequenceDiagram
+ participant Client A
+ participant Server A
+ Note over Client A: < Connect to mobile network >
+ Client A->>Server A: Client connects to server
+ Note over Client A: Upload e-mail 1
+ Client A->>Server A: Upload e-mail 1
+ Note over Client A: Upload e-mail 2
+ Client A->>Server A: Upload e-mail 2
+ Note over Client A: ⋮
+ Note over Client A: Upload e-mail 10
+ Client A->>Server A: Upload e-mail 10
+ Note over Client A: < Disconnect from mobile network >
+```
+
+The diagram illustrates the message flow for an e-mail upload scenario. It features two vertical lifelines: 'Client A' on the left and 'Server A' on the right. The sequence of messages is as follows: 1. Client A sends a message labeled '< Connect to mobile network >'. 2. Client A sends a message labeled 'Client connects to server' to Server A. 3. Client A sends 'Upload e-mail 1' to Server A. 4. Client A sends 'Upload e-mail 2' to Server A. 5. A vertical ellipsis (⋮) indicates multiple intermediate uploads. 6. Client A sends 'Upload e-mail 10' to Server A. 7. Client A sends a message labeled '< Disconnect from mobile network >'. The diagram is labeled 'E.804(14)\_F8-3' at the bottom right.
+
+Sequence diagram showing message flow for e-mail upload scenario between Client A and Server A.
+
+**Figure 8-3 – Message flow for e-mail upload scenario**
+
+##### 8.5.4.2.2 Download scenario
+
+The download test cycle is defined as follows:
+
+- 1) Connect to the mobile network (set up IP connectivity);
+- 2) Configurable pause (default 15 s);
+- 3) Login client A to server A;
+- 4) Configurable pause (default 15 s);
+- 5) Attempt to download all, e.g., ten e-mail header from server A, with a configurable pause (default 15 s) after each download;
+
+- 6) Configurable pause (default 15 s);
+- 7) Attempt to download up to, e.g., ten e-mails from server A, with a configurable pause (default 15 s) after each download;
+- 8) Disconnect from the mobile network;
+- 9) Configurable pause (default 15 s) before starting the next test sequence.
+
+Figure 8-4 shows message flow for e-mail download scenario.
+
+
+
+```
+
+sequenceDiagram
+ participant Client A
+ participant Server A
+ Note left of Client A: < Connect to mobile network >
+ Client A->>Server A: Client connects to server
+ Client A->>Server A: Download e-mail header 1
+ Note left of Client A: ⋮
+ Client A->>Server A: Download e-mail header 10
+ Note left of Client A:
+ Client A->>Server A: Download e-mail 1
+ Note left of Client A: ⋮
+ Client A->>Server A: Download e-mail 10
+ Note left of Client A: < Disconnect from mobile network >
+
+```
+
+E.804(14)\_F8-4
+
+Sequence diagram showing the message flow for an e-mail download scenario between Client A and Server A. The sequence starts with Client A sending a '< Connect to mobile network >' message to Server A, followed by 'Client connects to server'. Then, Client A sends 'Download e-mail header 1', followed by a vertical ellipsis, and then 'Download e-mail header 10'. Next, Client A sends 'Download e-mail 1', followed by a vertical ellipsis, and then 'Download e-mail 10'. Finally, Client A sends a '< Disconnect from mobile network >' message to Server A.
+
+**Figure 8-4 – Message flow for e-mail download scenario**
+
+### 8.5.4.2.3 End-to-end scenario with IMAP4
+
+For the end-to-end scenario it is necessary to point out the dependency of the measurement of the download parameters from the e-mail upload: Only in case of a successful upload by the A-party will the B-party be able to download the incoming e-mail (either by polling the inbox or after having received a notification from the e-mail server).
+
+The end-to-end test cycle is defined as follows:
+
+#### Initialization phase:
+
+- 1) The A-party and the B-party connect to the mobile network (set up IP connectivity);
+- 2) Configurable pause (default 15 s);
+- 3) Login client B to server B logs; client B then waits for incoming notifications.
+
+#### Upload phase:
+
+- 1) Client A connects to server A;
+- 2) Configurable pause (default 15 s);
+- 3) Upload up to ten e-mails to the server, with a configurable (default 15 s) pause after each upload;
+- 4) Disconnect from the mobile network;
+- 5) Configurable pause (default 15 s) before starting the next test sequence.
+
+#### Notification-Phase
+
+This phase runs in parallel to the upload phase.
+
+- 1) Client B is notified of incoming e-mails by server B and initiates the header download.
+
+- 2) Server B notifies client B of a new incoming e-mail.
+
+#### **Download phase:**
+
+This phase runs in parallel to the notification phase.
+
+- 1) Attempt to download all notified e-mails.
+
+#### **Disconnect phase:**
+
+- 1) The A-party and the B-party disconnect from the mobile network;
+- 2) Configurable pause (default 15 s) before starting the next test sequence.
+
+### **8.5.5 SDS**
+
+Definitions for short data service (SDS) are for further study.
+
+## **8.6 Data measurements**
+
+In the following clauses, data measurements for Internet-related services are described. While the process of obtaining a connection is different between circuit-switched and packet-switched access, there are many similarities for the actual data transfer phase.
+
+### **8.6.1 Common aspects**
+
+#### **8.6.1.1 Transaction definition and transaction types for data measurements**
+
+A transaction consists of access to a server to obtain content which is a closed unit from the user's perspective, e.g., a single downloaded file, or a web-site viewing access which may consist of several single objects which form the desired web page to be viewed.
+
+#### **8.6.1.2 Server types**
+
+The following categories of servers are distinguished; it is assumed that data service access is made to a server or entity within the general Internet domain:
+
+- Third-party content in the public Internet. This type of counterpart is termed as A-servers.
+- Accounts on Internet servers which are under control of the testing system or under control of testing personnel (e.g., web domains assigned for testing purposes). This type of counterpart shall be termed B-servers.
+- Special servers not reachable via public Internet (e.g., in the GGSN domain), or equipped with additional instrumentation, e.g., for IP-level tracing or non-Internet capabilities (e.g., UDP testing). This type of counterpart shall be termed C-servers.
+
+Access to a particular type of server will be termed using the same letter, e.g., A-access for access to an A-server.
+
+It is assumed that A-servers are generally outside the sphere of control of testing systems, in particular, no baseline information for load or other conditions is obtainable. In case of the B-servers and the C-servers, such control may exist, but will typically be limited in some way due to IP-security reasons, in particular if IP access is intra-network.
+
+Typically, when testing Internet services, availability may depend on influences other than those under test, and performance will be affected by third-party traffic. Meaningful tests therefore shall contain appropriate measures to exclude such effects from QoS assessment, or make sure that all networks under test are affected the same way. It is assumed that such tests are performed by fixed network units. Suggested methods are:
+
+- Cyclical availability checks on target servers or domains.
+- Cyclical access-time tests.
+
+It is assumed that for services where login is required, accounts used by the test system are valid, give positive login, and are good for full access to all activities forming the test.
+
+NOTE – This covers read/write privileges and directory-access rights (e.g., for FTP).
+
+User's point of view typically includes assumptions of a time the user is ready to wait before an action is considered to be failed. At the same time, IP service access typically goes with timeout windows at several levels, e.g., for inactivity over a certain period of time. Test design must combine these two aspects to a reasonable, technically feasible set of parameters.
+
+### 8.6.1.3 Test data content
+
+When using web content (web sites or single pages) for testing, it must be taken into account that such content will typically change frequently (e.g., content of popular web portals) and therefore performance tests may give varying results over time. Test design must ensure that such effects are excluded from QoS assessment. Preferably, standardized and constant web content shall be used.
+
+The degree of control a testing system has further depends on the type of service.
+
+With e-mail and FTP (assuming appropriate access privileges), data content can be determined exactly (e.g., by uploading files to be downloaded as part of subsequent tests).
+
+### 8.6.1.4 Transaction phase and parameter overview
+
+#### 8.6.1.4.1 General
+
+To ensure comparability and statistical validity of transactions, the following outer conditions need to be constant throughout a test case:
+
+- Access timing including behaviour in case of failure to obtain IP access. For reasons of comparability in density, it is required that all access attempts which form part of the statistics must follow a constant time pattern. Additional attempts, e.g., in the case of network or service unavailability must not affect the pattern of the statistically relevant access attempts.
+
+For services using buffering, such as video streaming, there may be the situation that while actual data transfer is already running, the visual appearance is that of still waiting. This period of time shall be considered part of service usage.
+
+Typically, in IP services periods of inactivity can occur with the session still intact. On the other hand, it must be taken into account that a useful PDP context is indicated by the system, which is in fact not useful. Therefore, testing shall include cyclical "lifecheck" measures. A possible method is Internet control message protocol (ICMP) pings to the DNS, not if such pings are blocked by the network, DNS accesses with dummy URL. Due to possible URL/IP address storage, it must be assured that actual DNS access takes place.
+
+Independent of the access type, the following general information elements need to be logged in order to ensure reproducibility and comparability of tests:
+
+- Operating system (type and version).
+- MTU size.
+- Logical location of server (e.g., public Internet or GGSN) with respect to the effects possibly created by the other traffic.
+- Maximum server throughput inbound and outbound, per session response per connection.
+
+#### 8.6.1.4.2 Packet-switched access
+
+Table 8-11 shows transaction phase and parameter overview for packet-switched access.
+
+**Table 8-11 – Transaction phase and parameter overview for packet-switched access**
+
+| Phase | Subphase | Parameters |
+|-----------------|----------------|------------------------------------------------------------------------------------------------------------------------|
+| Internet access | Network access | Equipment types and capabilities
Type of access used (CSD/PSD. UE initialization)
APN and other initial settings |
+| | Session access | Access and authentication parameters for the basic Internet access |
+| | DNS access | DNS (in case of non-automatic DNS assignment during session access procedure) |
+| | Domain access | Target URL, or server IP address
Account being used (where appropriate) |
+| Data transfer | | Content composition and size |
+| Cleardown | | Pause between access attempts |
+
+**8.6.1.4.3 Circuit-switched access**
+
+Table 8-12 shows transaction phase and parameter overview for circuit-switched access.
+
+**Table 8-12 – Transaction phase and parameter overview for circuit-switched access**
+
+| Phase | Subphase | Parameters |
+|-----------------|-------------------|-------------------------------------------------------------------------------|
+| Internet access | Connection set-up | Equipment types and capabilities
Parameters for dial-up connection |
+| | Session access | Access and authentication parameters for the basic Internet access |
+| | DNS access | DNS (in case of non-automatic DNS assignment during session access procedure) |
+| | Domain access | Target URL, or server IP address
Account being used (where appropriate) |
+| Data transfer | | Content composition and size |
+| Cleardown | | Pause between access attempts |
+
+**8.6.1.5 Possible transaction results**
+
+A data transaction is commonly termed "session" in contrast to "call" regardless of the type of connection (CS or PS). However, the general result term "dropped" shall be used, leading to "dropped session" for non-completed service usage.
+
+In case of dropped sessions, the testing system shall indicate which of the possible principal causes occurred for the purpose of deciding of the network under test is to be blamed or not:
+
+- Loss of radio connection.
+- Loss of basic IP connection. If DNS access is still possible after a session loss, it is assumed that the basic IP connection is still intact.
+- Loss of Internet access: If access to another domain or server is still possible, it shall be assumed that basic Internet services are still available.
+- Loss of connection to the server.
+
+For the single phases of Internet access (as part of general service access), the following additional definitions are given:
+
+- **Session access:** Access and authentication procedure for basic Internet access. Successful access is assumed when a temporary IP address good for data transactions has been assigned to the testing system.
+- **DNS access:** Obtaining an IP address from a URL (web site name, server name). Successful DNS access is assumed when an IP address has been obtained from a given, valid URL.
+- **Domain access:** This is the actual Internet access as seen from the user's perspective. For testing purposes, a successful domain access can be assumed when communication with the target server has been proved. The respective action will depend on the service under test.
+
+#### **8.6.1.6 Content quality**
+
+Content quality relates to the quality as perceived by the user.
+
+#### **8.6.1.7 Content integrity**
+
+Content integrity is correctness and completeness.
+
+Technical precautions should be taken to make sure that the content received is the one expected.
+
+### **8.6.2 FTP**
+
+#### **8.6.2.1 Transaction definition and transaction types for FTP**
+
+The basic transaction for FTP testing consists of Internet access to a FTP server followed by either downloading or uploading a single file of given size.
+
+It is understood that basic access procedures such as server login shall not be considered as part of a transaction.
+
+For download tests, it must be assured that the file to be downloaded is actually available on the server. For upload tests, it must be assured that no storage-size limitations prevent successful upload.
+
+#### **8.6.2.2 Transaction phase and parameter overview for FTP**
+
+Same as clause 8.6.1.
+
+In addition, the following parameters shall be logged:
+
+- type of FTP client used;
+- protocol used (TCP/IP or UDP);
+- type used (active or passive).
+
+#### **8.6.2.3 Possible transaction results for FTP**
+
+Same as clause 8.6.1.
+
+#### **8.6.2.4 Content quality for FTP**
+
+Same as clause 8.6.1.
+
+#### **8.6.2.5 Content integrity for FTP**
+
+Same as clause 8.6.1. It is recommended to use file size and checksum comparison.
+
+### **8.6.3 HTTP**
+
+#### **8.6.3.1 Transaction definition and transaction types for HTTP**
+
+The basic transaction for HTTP testing consists of Internet access followed by downloading a web site for a given URL response of given structure.
+
+### **8.6.3.2 Transaction phase and parameter overview for HTTP**
+
+Same as clause 8.6.1.
+
+In addition, the following parameters shall be logged:
+
+- Type of browser used (including version number/build). For test-system browsers, maximum number of parallel socket connections.
+- Reference web site used (e.g., structure, size of the documents, etc.).
+
+### **8.6.3.3 Possible transaction results for HTTP**
+
+Same as clause 8.6.1.
+
+### **8.6.3.4 Content quality for HTTP**
+
+Same as clause 8.6.1.
+
+### **8.6.3.5 Content integrity for HTTP**
+
+It is assumed that a typical HTTP downloaded page consists of a main page and a number of sub-elements contained in this page (web elements). Based on the definitions in clause 8.6.1, recommended method for content integrity checking is to verify that the expected main page has been loaded, and to verify the expected number of web elements and their respective type, size and content. (see also Appendix VII).
+
+QoS parameters shall only be compared directly (e.g., in benchmarks) if they are obtained without accelerators (performance enhancement proxies) or with the same accelerator behaviour for all networks involved. In practice, since the exact working of such accelerators is not known, this leads to the requirement that download times shall only be compared directly if such accelerators are not in the chain. Otherwise, when reporting such values the fact that different accelerator operation was in effect and that values are not to be compared shall be explicitly emphasized.
+
+## **8.6.4 E-mail**
+
+Refer to clause 8.5.4 as the procedures described there are usable for direct services as well if notification is disabled on the e-mail server.
+
+## **8.6.5 WAP**
+
+### **8.6.5.1 Transaction definition and transaction types for WAP**
+
+The basic transaction for WAP testing consists of WAP access and download of a WAP page.
+
+To guarantee comparability and statistical validity of transactions, the following parameters have to be logged:
+
+- Protocol (WAP1.x or WAP2.0).
+ - Access-point (gateway).
+ - Type of mobile used/emulated (user-agent string, accept-header).
+ - Mobile browser (version number, build).
+ - Depending on the test goal, one of the following clauses should be used:
+ - A given reference WAP page (static WAP content)
+Picture count and text size has to be stable.
+ - A static url, e.g., main page of the WAP-portal (variable WAP content)
+Picture count and text size have to fulfil minimum requirements depending on the url.
+- NOTE 1 – It is required that the WAP page has a size greater than one packet.
+- It is required that all WAP session attempts follow a constant time pattern.
+
+NOTE 2 – The size of the mobile cache can be neglected, because all tests should be performed with an empty mobile cache.
+
+The mobile specific effects (rendering) are not represented in case of non-application level testing.
+
+### 8.6.5.2 Transaction phase and parameter overview for WAP
+
+Same as clause 8.6.1.
+
+In addition, the following parameters shall be logged:
+
+- Protocol (WAP1.x or WAP2.0).
+- Access-point (gateway).
+- mobile used (user-agent string, accept-header, browser type).
+- Structure of the WAP page (e.g., size of content and/or number of pictures).
+- URL of the WAP page.
+
+When measuring the parameter using drive tests or field tests, it is recommended that the following general method, shown in Figure 8-5, be used.
+
+
+
+```
+graph TD; A[Technology available (precondition)] --> B[PS attach (precondition)]; B --> C[Clear the cache]; C --> D[Request the first page]; D --> E[PDP context activation]; E --> F[WAP activation (WAP1.X)
+WAP IP access (WAP2.0)]; F --> G[Transfer the first content-page]; G --> H{Next page?}; H -- No --> I[WAP de-activation (WAP1.X)]; I --> J[PDP context de-activation]; J --> K[PS detach]; K --> A; H -- Yes --> L[Clear the cache]; L --> M[Request the page]; M --> N[WAP IP access (WAP2.0)]; N --> O[Transfer the page]; O --> L;
+```
+
+The flowchart illustrates the general method for the transaction phase and parameter overview for WAP. It begins with 'Technology available (precondition)' and 'PS attach (precondition)', both leading to 'Clear the cache'. From 'Clear the cache', the process moves to 'Request the first page', which then leads to 'PDP context activation'. This is followed by 'WAP activation (WAP1.X) / WAP IP access (WAP2.0)', which leads to 'Transfer the first content-page'. A decision diamond 'Next page?' follows. If 'No', the process proceeds to 'WAP de-activation (WAP1.X)', 'PDP context de-activation', and 'PS detach', which loops back to the start. If 'Yes', the process proceeds to 'Clear the cache', 'Request the page', 'WAP IP access (WAP2.0)', and 'Transfer the page', which loops back to the 'Clear the cache' step. The label 'E.804(14)\_F8-5' is present in the bottom right of the diagram.
+
+Flowchart of the general method for transaction phase and parameter overview for WAP.
+
+Figure 8-5 – General method for transaction phase and parameter overview for WAP
+
+### 8.6.5.3 Possible transaction results for WAP
+
+Same as clause 8.6.1.
+
+#### **8.6.5.4 Content quality for WAP**
+
+Same as clause 8.6.1.
+
+#### **8.6.5.5 Content integrity for WAP**
+
+The following is to verify that the received page is not an "error message" page and that the requested page is downloaded.
+
+It is assumed that a typical WAP downloaded page consists of a main page and a number of sub-elements contained in this page, which may have sub-elements themselves. Based on the definitions in clause 8.6.1, recommended method for content integrity checking is to verify that all elements of the expected main page have been completely loaded.
+
+- In case of testing a given reference WAP page (static WAP content), the whole (known) content should be verified.
+
+In case of testing public WAP pages with variable WAP content, it is necessary to define and check the page regarding minimum criteria (e.g., five pictures and text size greater than 150 Byte). For identifying the content of the requested page it is necessary to check static page content (logo, keyword).
+
+### **8.6.6 Streaming video**
+
+#### **8.6.6.1 Transaction definition and transaction types for streaming video**
+
+The basic transaction for streaming video testing consists of Internet access to a streaming server followed by replay access to a given content from this server.
+
+#### **8.6.6.2 Transaction phase and parameter overview for streaming video**
+
+Same as clause 8.6.1.
+
+In addition, the following parameters shall be logged:
+
+- streaming server and client versions used;
+- Operating system (OS) and relevant configuration of streaming server;
+- streaming protocol used.
+
+#### **8.6.6.3 Possible transaction results for streaming video**
+
+Same as clause 8.6.1.
+
+#### **8.6.6.4 Content quality for streaming video**
+
+To be decided.
+
+#### **8.6.6.5 Content integrity for streaming video**
+
+To be decided.
+
+### **8.6.7 Media download**
+
+#### **8.6.7.1 Transaction definition and types for media download**
+
+There is a need for content providers and operators to control the usage of downloaded media objects. Download is the means by which a media object is delivered to the UE. Digital rights management (DRM) is the means to control the usage of the media object once it has been downloaded.
+
+DRM enables content providers to define rules (rights) for how the media object should be used. It is possible to associate different rights with a single media object. Different rights may have different prices. A content provider can grant a user the rights to preview media objects for free and charge the user only for the full usage rights. Since the value lies in the rights and not in the media object itself, DRM makes it possible to sell the rights to use the media object, rather than selling the media object
+
+itself. The rights can be delivered to the consuming UE by downloading them together with the content or by sending the rights object separately from content. The former case (combined delivery) is simpler whereas the latter case (separate delivery) provides more security by making it more difficult to steal the content.
+
+#### 8.6.7.1.1 OMA network elements
+
+Figure 8-6 shows OMA network elements.
+
+
+
+```
+
+graph LR
+ subgraph Device
+ DA[Discovery application]
+ DN[Download agent]
+ DR[DRM agent]
+ end
+ subgraph Network
+ PS[Presentation server]
+ DS[Download server]
+ SR[Status report server]
+ DP[DRM packager]
+ end
+ DA --- PS
+ DN --- DS
+ DN --- SR
+ DS --- DP
+
+```
+
+The diagram illustrates the OMA network elements, categorized into 'Device' and 'Network'. On the 'Device' side, there are three components: 'Discovery application', 'Download agent', and 'DRM agent'. On the 'Network' side, there are four components: 'Presentation server', 'Download server', 'Status report server', and 'DRM packager'. Connections are shown as follows: 'Discovery application' connects to 'Presentation server'; 'Download agent' connects to both 'Download server' and 'Status report server'; and 'Download server' connects to 'DRM packager'.
+
+Diagram of OMA network elements showing Device and Network components and their connections.
+
+E.804(14)\_F8-6
+
+**Figure 8-6 – OMA network elements**
+
+Download descriptor can be located on either the presentation server or the download server.
+
+**Discovery application:** The user discovers media objects on the web by using a WAP browser or applications specifically created for one type of media.
+
+**Download agent:** Launched after the discovery application downloaded the download descriptor and handles the remaining part of the download.
+
+**DRM agent:** Handles DRM media objects.
+
+**Presentation server:** May be a web or WAP "portal".
+
+**Download server:** Is responsible for the download transaction, moving the actual media object from the server to the download agent.
+
+**Status report server:** Receives the posted status reports.
+
+**DRM packager:** Is wrapping media objects into DRM containers and is also responsible for generating the rights.
+
+#### 8.6.7.1.2 OMA download use cases
+
+##### 8.6.7.1.2.1 Combined OMA download use case
+
+Figure 8-7 shows combined OMA download use case.
+
+
+
+- 1: Client finds a markup page that exposes an attractive service.
+- 2: Client selects a URI that points to a download descriptor in the download server.
+ - The download descriptor is delivered to the client. The download descriptor includes an URI referencing the media object.
+ - The media object is delivered from the download server.
+- 3: Client reports the status of download transaction to the download server.
+
+```
+
+splash
+http://a.b.c/s?id=45
+1780
+image/GIF
+http://a.b.c/in?id=45
+
+```
+
+E.804(14)\_F8-7
+
+MS
+ Optional WAP GW or HTTP proxy
+ Discovery Doc. (XHTML, URL)
+ Media object
+ META information (download descriptor)
+ Install-Notify
+
+Diagram of Combined OMA download use case showing interactions between MS, Presentation server, Download server, Content storage, and Status report server. Step 1: MS sends Discovery Doc to Presentation server. Step 2: MS sends GET request to Download server, which returns a multipart response containing META information (download descriptor) and Media object. Step 3: MS sends Install-Notify to Status report server. A sample download descriptor XML is shown.
+
+**Figure 8-7 – Combined OMA download use case**
+
+The OMA download use cases extend the basic HTTP download use case by using a download descriptor. In the combined OMA download use case, a media object and a download descriptor is downloaded from an HTTP server by using one GET request and response. Because the media object and the download descriptor are both delivered together, the user is unable to confirm the download before the delivery of the media object. Optionally, a status report is posted to a URL specified in the download descriptor.
+
+When this use case starts, the media object and the corresponding download descriptor is packaged into one multipart entity available on an HTTP URL, the URL must also be available on the device to the user (e.g., as a link in a WAP page). When this case ends, if the main scenario is completed, the media object is available on the device.
+
+Main scenario:
+
+- 1) User initiates a GET request to the URL – by selecting a link in a web page, for example.
+- 2) Client sends GET request to the server and waits for a response.
+- 3) Server serves up the requested resource, the multipart with the media object and the download descriptor, and sends a GET response back to client.
+- 4) Client accepts GET response, with the HTTP headers and the data, from the server.
+- 5) The information in the download descriptor is presented to the user.
+- 6) If the download descriptor indicates that a status report shall be posted, a status report is posted to the specified URL.
+- 7) The media object is made available to the user (e.g., saved on the file system).
+
+**8.6.7.1.2.2 Separate OMA download use case**
+
+Figure 8-8 shows separate OMA download use case.
+
+
+
+- 1: Client finds a markup page that exposes an attractive service.
+- 2: Client selects an URI that points to a download descriptor in the download server.
+ - The download descriptor is delivered to the client. The download descriptor includes an URI that references the media object.
+- 3: Client retrieves the media object file from the download server.
+- 4: Client reports the status of download transaction to the download server.
+
+```
+
+splash
+http://a.b.c/s?id=45
+1780
+image/GIF
+http://a.b.c/in?id=45
+
+
+```
+
+E.804(14)\_F8-8
+
+MS
+ Optional WAP GW or HTTP proxy
+ Presentation server
+ Download server
+ Status report server
+ Content storage
+
+Discovery Doc. (XHTML, URL)
+ Media object
+ META information (download descriptor)
+ Install-Notify
+
+Sequence diagram for Separate OMA download use case. It shows interactions between a Mobile Station (MS), an Optional WAP GW or HTTP proxy, a Presentation server, a Download server, a Status report server, and Content storage. The sequence involves four steps: 1. MS sends Discovery Doc. to Presentation server; 2. MS sends META information to Download server; 3. MS sends Media object request to Download server; 4. MS sends Install-Notify to Status report server. A sample XML for the download descriptor is shown on the right.
+
+**Figure 8-8 – Separate OMA download use case**
+
+In the separate OMA download use case, a media object and a download descriptor is downloaded from an HTTP server by using two GET request and response. The user is able to confirm the download based on pre-download capability checks in the device, and via a device specific download user interface. Optionally, a status report is posted to a URL specified in the download descriptor.
+
+When this use case starts, the media object and download descriptor are available on two separate HTTP URLs, the download descriptor URL must also be available on the device to the user (e.g., as a link in a WAP page). The media object URL is available on the download descriptor. When this case ends, if the main scenario is completed, the media object is available on the device.
+
+Main scenario:
+
+- 1) User initiates a GET request to the URL – by selecting a link in a web page, for example.
+- 2) Client sends GET request to the server and waits for a response.
+- 3) Server serves up the requested resource, the download descriptor, and sends GET response back to client.
+- 4) Client accepts GET response, with the HTTP headers and the data, from the server.
+- 5) The information in the download descriptor is analysed by the UE (capability checks) and user is given a chance to confirm the download.
+- 6) HTTP download is used to deliver the media object.
+- 7) If the download descriptor indicates that a status report shall be posted, a status report is posted to the specified URL.
+- 8) The media object is made available to the user (e.g., saved on the file system).
+
+**8.6.7.1.3 DRM use cases**
+
+**8.6.7.1.3.1 Combined delivery DRM use case**
+
+Figure 8-9 shows combined delivery DRM use case.
+
+
+
+The diagram illustrates the combined delivery DRM use case. On the left, an 'Application server' box contains three elements: 'Media object', 'Rights', and a circle labeled 'HTTP server'. An arrow labeled 'HTTP' points from the 'HTTP server' circle to a 'Mobile device' icon on the right. Below the mobile device is the label 'E.804(14)\_F8-9'.
+
+Figure 8-9: Combined delivery DRM use case diagram. An Application server contains a Media object, Rights, and an HTTP server. An arrow labeled 'HTTP' points from the HTTP server to a Mobile device. The diagram is labeled E.804(14)\_F8-9.
+
+**Figure 8-9 – Combined delivery DRM use case**
+
+A protected media object, together with the corresponding rights object, is downloaded from an HTTP server. When this case ends, if the main scenario is completed, the media object is available on the UE. It can be used only according to the granted usage rights.
+
+Main scenario: The content provider, using a DRM packager, packages the media object and the rights object into one DRM message. The DRM message is made available to the UE (e.g., by publishing it on a web page). HTTP download or OMA download is used to download the DRM message (media and rights object). The user is using the media object and the DRM agent ensures that it is used according to the rights.
+
+### 8.6.7.1.3.2 Separate delivery DRM use case
+
+Figure 8-10 shows separate delivery DRM use case.
+
+
+
+The diagram illustrates the separate delivery DRM use case. On the left, an 'Application server' box contains four elements: 'Media object', 'Rights', a circle labeled 'HTTP server', and a circle labeled 'Push initiator'. An arrow labeled 'HTTP' points from the 'HTTP server' circle to a 'Mobile device' icon on the right. Another arrow labeled 'Push access protocol' points from the 'Push initiator' circle to a 'Push proxy gateway' box. From the 'Push proxy gateway', an arrow labeled 'Push over-the-air protocol' points to the 'Mobile device'. Below the mobile device is the label 'E.804(14)\_F8-10'.
+
+Figure 8-10: Separate delivery DRM use case diagram. An Application server contains a Media object, Rights, an HTTP server, and a Push initiator. Arrows show HTTP from the HTTP server to a Mobile device, and Push access protocol from the Push initiator to a Push proxy gateway, which then uses Push over-the-air protocol to reach the Mobile device. The diagram is labeled E.804(14)\_F8-10.
+
+**Figure 8-10 – Separate delivery DRM use case**
+
+A protected media object is downloaded from an HTTP server. Later, the corresponding rights object is delivered to the UE via WAP Push message (e.g., over SMS). When this case ends, if the main scenario is completed, the media object is available on the UE. It can be used only according to the granted usage rights.
+
+Main scenario: The content provider, using a DRM packager, packages the encrypted media object into a DRM content format. The key to decrypt the media object is put into the rights object. The DRM content format URL is made available to the UE (e.g., by publishing it on a web page). The rights object is not available to the UE. The HTTP download or OMA download is used to deliver the DRM content format. The user waits for the rights object to be delivered via a WAP Push message from the content provider. (If OMA download was used, the status report could be used as a "trigger" in the network to push the rights object to the UE, after a successful download of the encrypted media object). The media object is decrypted using the key from the rights. The user is using the media object and the DRM agent ensures that it is used according to the rights.
+
+## 8.6.7.2 Transaction phase and parameter overview for media download
+
+### 8.6.7.2.1 Overview of basic parameters for media download
+
+Charts in Figure 8-11 (taken from [IETF RFC 3481]) show on which basic parameters the media download service sequence is based.
+
+
+
+| | | | | | | |
+|----------------------------------------------|------------------------------------------|----------------------------------|---------------------------------------------------------------------|--------------------------------------|--------------------------------------------|---------------------------------|
+| Parameters | | | WAP {Page} session failure ratio [%]
WAP {Page} session time [s] | | | Next page etc. |
+| | | | 1
WAP {Page} mean data rate [kbit/s] | | | |
+| | PDP context activation failure ratio [%] | WAP activation failure ratio [%] | WAP {Page} IP access failure ratio [%] | WAP {Page} request failure ratio [%] | WAP {Page} data transfer cut-off ratio [%] | |
+| | PDP context activation time [s] | WAP activation time [s] | WAP {Page} IP set-up time [s] | WAP {Page} request time [s] | WAP {Page} data transfer time [s] | |
+| Trigger points from customer's point of view | | | | | | |
+| Push WAP-Portal access button | | | | | | |
+| Select bookmark | | | | | | |
+| | | (Only in case of WAP 1.x) | (Only in case of WAP 2) | | | |
+| | | | | | WAP-Portal page completely loaded | Selected page completely loaded |
+| | | | | | Selection of a WAP page link | Selection of a WAP page link |
+
+E.804(14)\_F8-11
+
+**Figure 8-11 – WAP or equivalent parameters for download part**
+
+For the download part, the marked WAP or equivalent HTTP parameters should be used.
+
+Figure 8-12 provides store-and-forward parameters for DRM separate delivery part.
+
+
+
+The diagram illustrates the store-and-forward parameters for DRM separate delivery part, showing interactions between the A-party side and the B-party side across 9 time points (t1-t9).
+
+**A-party side:**
+
+- Parameters:**
+ - Message upload session FR
+ - Message upload session time
+ - Message upload access FR
+ - Message upload data transfer cut-off ratio
+ - Message upload access time
+ - Message upload data transfer time
+- Trigger point from user's point of view:**
+ - t1: Push "send" message button at A-party side
+ - t2: See start of message upload at A-party side
+ - t3: See end of message upload at A-party side
+- Technical trigger points for success cases:**
+ - t1: First data packet is uploaded by the A-party side
+ - t2: Upload of the first data packet containing content
+ - t3: Reception of final acknowledgement for the last data packet containing content
+
+**B-party side:**
+
+- Technical trigger points for success cases:**
+ - t4: Notification download starts
+ - t5: Reception of the first data packet containing notification content
+ - t6: Notification is received
+ - t7: Client requests the message
+ - t8: Reception of the first data packet containing message content
+ - t9: Message is received completely at B-party side
+- Trigger point from user's point of view:**
+ - t4: See start of notification download at B-party side
+ - t5: See first incoming notification data at B-party side
+ - t6: See notification is received at B-party side
+ - t7: Start further message download at B-party side
+ - t8: See incoming message data at B-party side
+ - t9: See complete message at B-party side
+- Parameters:**
+ - Notification start FR
+ - Notification start time
+ - Notification download access FR
+ - Notification download IP access time
+ - Notification data transfer cut-off ratio
+ - Notification data transfer time
+ - Notification download FR
+ - Notification download time
+ - Notification and message download FR
+ - Notification and message download time
+ - Message download access FR
+ - Message download time
+ - Message download FR
+ - Message download time
+
+**End-to-end metrics:**
+
+- End-to-end failure ratio
+- End-to-end time
+
+Sequence diagram showing store-and-forward parameters for DRM separate delivery part between A-party and B-party sides. The diagram includes parameters like Message upload session FR, Notification download starts, and various time and failure rate metrics across 9 time points (t1-t9).
+
+NOTE – FR: Failure rate
+
+E.804(14)\_F8-12
+
+NOTE – FR: Failure rate
+
+Figure 8-12 – Store-and-forward parameters for DRM separate delivery part
+
+For the DRM separate delivery part, the marked store-and-forward parameters should be used.
+
+#### 8.6.7.2.2 Media download subphases
+
+The following text boxes are providing subphase specific information about the whole media download service sequence. The information includes the basic parameters used and the trigger points they are based on.
+
+##### A: Purchase link
+
+| Trigger point from user's point of view | Technical description/protocol part |
+|------------------------------------------------------------------------------------|-------------------------------------------------------------|
+| Start: Select download purchase link. | Start:
• Same as WAP/HTTP |
+| Stop: Payment receipt page is successfully loaded within the specified time limit. | Stop:
• Same as WAP/HTTP (content: payment receipt page) |
+
+##### B: Download descriptor
+
+| Trigger point from user's point of view | Technical description/protocol part |
+|----------------------------------------------------|------------------------------------------------------------|
+| Start: Appearance of the payment receipt page. | Start:
• Same as WAP/HTTP (Stop from A) |
+| Stop: Media file data page is successfully loaded. | Stop:
• Same as WAP/HTTP (content: Download descriptor) |
+
+##### C: Media object
+
+| Trigger point from user's point of view | Technical description/protocol part |
+|-----------------------------------------|-----------------------------------------------------|
+| Start: Push download button. | Start:
• Same as WAP/HTTP (Stop from B) |
+| Stop: Media file download is completed. | Stop:
• Same as WAP/HTTP (content: Media object) |
+
+##### D: Media and DRM object
+
+| Trigger point from user's point of view | Technical description/protocol part |
+|-----------------------------------------|-------------------------------------------------------------|
+| Start: Push download button. | Start:
• Same as WAP/HTTP (Stop from B) |
+| Stop: Media file download is completed. | Stop:
• Same as WAP/HTTP (content: Media and DRM object) |
+
+**E: Media object and download descriptor**
+
+| Trigger point from user's point of view | Technical description/protocol part |
+|-----------------------------------------------|-----------------------------------------------------------------------------|
+| Start: Appearance of the payment receipt page | Start:
• Same as WAP/HTTP (Stop from A) |
+| Stop: Media file download is completed | Stop:
• Same as WAP/HTTP (content: Media object and Download Descriptor) |
+
+**F: Media and DRM object and download descriptor**
+
+| Trigger point from user's point of view | Technical description/protocol part |
+|-----------------------------------------------|-------------------------------------------------------------------------------------|
+| Start: Appearance of the payment receipt page | Start:
• Same as WAP/HTTP (Stop from A) |
+| Stop: Media file download is completed. | Stop:
• Same as WAP/HTTP (content: Media and DRM object and download descriptor) |
+
+**G: Install notify**
+
+| Trigger point from user's point of view | Technical description/protocol part |
+|-------------------------------------------------------------------------------------------|-------------------------------------------------------|
+| Start: Push save or install button if content is not saved automatically. | Start:
• Same as WAP/HTTP (Stop from C, D, E or F) |
+| Stop: Downloaded file is successfully saved or installed within the specified time limit. | Stop:
• Sending of the Install Notify message |
+
+**H: DRM object**
+
+| Trigger point from user's point of view | Technical description/protocol part |
+|--------------------------------------------------------------------------------------------|---------------------------------------------------------|
+| Start: Downloaded file is successfully saved or installed within the specified time limit. | Start:
• Sending of the Install Notify (Stop from G) |
+| Stop: DRM Message is received | Stop:
• Same as store-and-forward |
+
+**8.6.7.2.3 Combined OMA download and combined delivery DRM use case**
+
+The overview chart shown in Figure 8-13 illustrates the minimum combination of subphases for a media download service sequence in case of combined OMA download and combined delivery DRM use case.
+
+
+
+The diagram consists of three identical yellow rectangular boxes arranged horizontally. Each box contains the number '1' in the center. Below the boxes, the letters 'A', 'F', and 'G' are placed, each centered under its respective box.
+
+Diagram showing three yellow boxes labeled 1, A, F, and G. The boxes are arranged horizontally with '1' inside each box. Below the boxes are the labels 'A', 'F', and 'G' respectively.
+
+**Figure 8-13 – Combined OMA download and combined delivery DRM use case**
+
+#### 8.6.7.2.4 Separate OMA download and combined delivery DRM use case
+
+The overview chart shown in Figure 8-14 illustrates the minimum combination of subphases for a media download service sequence in case of separate OMA download and combined delivery DRM use case.
+
+
+
+A diagram showing four yellow rectangular boxes arranged horizontally. Each box has a blue border and contains the number '1'. Below each box is a label: 'A', 'B', 'D', and 'G' respectively.
+
+Figure 8-14: Separate OMA download and combined delivery DRM use case diagram
+
+Figure 8-14 – Separate OMA download and combined delivery DRM use case
+
+#### 8.6.7.2.5 Combined OMA download and separate delivery DRM use case
+
+The overview chart shown in Figure 8-15 illustrates the minimum combination of subphases for a media download service sequence in case of combined OMA download and separate delivery DRM use case.
+
+
+
+A diagram showing four yellow rectangular boxes arranged horizontally. The first three boxes have blue borders and contain the number '1'. The fourth box has a red border and contains the number '2'. Below each box is a label: 'A', 'E', 'G', and 'H' respectively.
+
+Figure 8-15: Combined OMA download and separate delivery DRM use case diagram
+
+Figure 8-15 – Combined OMA download and separate delivery DRM use case
+
+#### 8.6.7.2.6 Separate OMA download and separate delivery DRM use case
+
+The overview chart shown in Figure 8-16 illustrates the minimum combination of subphases for a media download service sequence in case of separate OMA download and separate delivery DRM use case.
+
+
+
+A diagram showing five yellow rectangular boxes arranged horizontally. The first four boxes have blue borders and contain the number '1'. The fifth box has a red border and contains the number '2'. Below each box is a label: 'A', 'B', 'C', 'G', and 'H' respectively.
+
+Figure 8-16: Separate OMA download and separate delivery DRM use case diagram
+
+Figure 8-16 – Separate OMA download and separate delivery DRM use case
+
+#### 8.6.7.2.7 Additional information about parameters for media download
+
+Same as clause 8.6.1 (WAP/HTTP/store-and-forward).
+
+Media download session time as the sum over all the subphase times (whole sequence/session was successful).
+
+Media download session failure ratio over all the subphases.
+
+In addition, the following information should be logged:
+
+- Use case information.
+- Media object information: Artist, title, format and size.
+
+#### **8.6.7.2.8 Recommended testing method for media download**
+
+Test objects for media download should not be reported to chart ranking agencies and billed with real costs. They should be endlessly purchasable without impact on service sequence.
+
+When measuring the parameters using drive tests or field tests, it is recommended that for example the general WAP method be used. In case of separate delivery DRM use case, the SMS memory should be erased before sequence starts and be checked at the end of the service sequence.
+
+NOTE – Frequently, usage of public content can take influence on official chart rankings. Purchase of an already purchased song can change the service sequence.
+
+#### **8.6.7.3 Possible transaction results for media download**
+
+Same as clause 8.6.1 (WAP/HTTP/store-and-forward).
+
+Media download session time as the sum over all the subphase times (when whole sequence/session was successful).
+
+Media download session failure ratio over all the subphases.
+
+#### **8.6.7.4 Content quality for media download**
+
+Same as clause 8.6.1 (WAP/HTTP/store-and-forward).
+
+NOTE – On the application layer the content quality for media download could be evaluated by playback of downloaded media.
+
+#### **8.6.7.5 Content integrity for media download**
+
+Same as clause 8.6.1 (WAP/HTTP/store-and-forward).
+
+NOTE – On the application layer the content integrity for media download could be evaluated by playback of downloaded media.
+
+### **9 Requirements for quality of service measurement equipment**
+
+#### **9.1 Overview**
+
+##### **9.1.1 General aspects**
+
+All tests are based on emulation of a typical user using services provided in a public mobile network (PMN). All of the services to be tested (see clause 7) can be emulated by the mobile QoS test equipment (MQT) which can be installed in a vehicle, can be carried around by a pedestrian or is installed for semi-stationary use (e.g., office environment).
+
+Test scenarios need to distinguish the following principal user cases.
+
+- 1) User-to-user services (typically telephony).
+- 2) Store-and-forward services (e.g. SMS).
+- 3) Information services (e.g., accessing the Internet or FTP download).
+- 4) Push services.
+
+Figure 9-1 provides an overview of a QoS test scenario.
+
+
+
+MQT : Mobile QoS test-equipment
+FQT : Fixed QoS test-equipment
+
+E.804(14)\_F9-1
+
+Figure 9-1 – QoS test scenario overview. The diagram shows a Mobile QoS test-equipment (MQT) connected to two packet networks (PMN #1 and PMN #2) under test. These networks are connected to a central switching node, which is also connected to a PSTN/ISDN network and a Packet data network. The PSTN/ISDN and Packet data network are both connected to a Fixed QoS test-equipment (FQT). A legend below the diagram defines MQT as Mobile QoS test-equipment and FQT as Fixed QoS test-equipment. The diagram is labeled E.804(14)\_F9-1.
+
+**Figure 9-1 – QoS test scenario overview**
+
+Some of the services require test-equipment connected to a non-mobile network emulating the counterpart of the typical mobile user or the host offering the service. This part will be called fixed QoS test-equipment (FQT). The FQT may be connected via a public network (e.g., PSTN, ISDN or PDN) or via a network internal connection point (e.g., at MSC). The FQT for type 3) and 4) services could be composed as a (virtual) Internet service provider (ISP).
+
+Below, requirements will be described on a per scenario basis. Those requirements not belonging to a specific scenario, e.g., antenna requirements will be grouped together.
+
+Depending on how far the MQT can be automated or not, there is distinction between:
+
+- MQT-LC: local control and operation; or
+- MQT-RC: remote control and operation.
+
+Although the same type of classification (-LC or -RC) can be made for FQT, most of the FQT are remote controlled.
+
+### 9.1.2 Considerations on trigger points
+
+Without loss of generality, it can be assumed that any feasible test equipment will contain some kind of communication terminal (UE) which may be a special type (e.g., a trace phone) or a standard UE. Also, it can be assumed that each of such devices will provide information from different communication layers, from application layer (close to the user interface) down to lower layers, e.g., operating-system events, TCP/IP layer, or layer 3 signalling information, which is used as trigger points for QoS parameters processing.
+
+When considering the event chain, action is typically triggered by some emulated user action which finally causes some action on the air interface. This process of event propagation is deterministic, allowing some kind of mapping between layers, in the limits of available information, but will inevitably be associated with some communication and processing delay in each stage.
+
+Therefore, choice of the layer from which to get trigger point information determines the view expressed in a QoS parameters. Generally, choosing lower-level events such as layer 3 gives a more network-centric view, while events on higher levels tend to produce views more user-related. From this, the following guidelines result:
+
+- Within the same QoS parameters, the source layer for events used as trigger points should be the same.
+- In benchmarking, all networks under test should be tested using the same type of UE, and QoS parameters for all networks under test should use trigger points from the same layer. When changing the source layer for a given trigger point, changes in QoS parameters should be expected, and respective calibration measurements should be taken to assess influence on QoS parameters both quantitatively and qualitatively.
+
+
+
+- Versions of measurement equipment:
+ - Hardware version.
+ - Software version of measurement application.
+ - Operating system version (operation system and service pack).
+- Date, time of day (UTC time + time zone).
+
+Manually entered information:
+
+- User.
+- Comment.
+- All other information which cannot be collected automatically, on the required test case control parameters, to re-run the test case under the same conditions.
+
+#### **9.2.3.1.2 Information on user equipment in use**
+
+For the set-up of the user equipment (UE) in use, the following list represents the minimum required parameters:
+
+Information automatically collected:
+
+- Type of user equipment.
+- Firmware version.
+- Unique UE ID (e.g., IMEI, serial number, MAC, etc.).
+- IMSI (configuration of SIM card can have a significant influence on the measurement result).
+- Software version of driver for operating system, if used.
+- All settings of the control software.
+
+Manually entered information:
+
+- Antenna:
+ - Type.
+ - Extra attenuation.
+ - Total cable loss (e.g., Cables, RF combiners, etc.).
+
+#### **9.2.3.1.3 Information on store-and-forward set-ups**
+
+The following information has to be logged:
+
+- Number of service centre.
+- Access parameters.
+- Transmitted message, video and/or audio.
+- Timeout values.
+
+#### **9.2.3.1.4 Information on data test set-ups**
+
+For data tests, the following list represents the minimum required parameters.
+
+Information automatically collected:
+
+- Any stack parameter configuration or difference to the standard of the used operating system, e.g., information about TCP stack parameter changes.
+- Servers.
+- All settings of the control software concerning the data test set-up, e.g., FTP settings.
+
+#### **9.2.3.2 Measurement data**
+
+Each measured item has to be stored with the corresponding timestamp.
+
+In addition to the trigger points specified in clause 7, the measurement equipment shall collect the following list of data:
+
+- Network ID (MCC, MNC, CI, LAC), respective data items with a rate sufficient to track the user equipments behaviour.
+
+### 9.2.3.3 Status information
+
+The system has to record information about the status and progress of the current measurement.
+
+### 9.2.3.4 Trigger points
+
+The system has to record all necessary trigger points. See clause 7.
+
+### 9.2.3.5 QoS parameters
+
+If possible, QoS parameters shall be calculated during the measurement and be shown on the man machine interface (MMI).
+
+For test cases on distributed systems, the calculation process for QoS parameters has to be done in a post process.
+
+## 9.2.4 Test-UE
+
+Basic requirements on the Test-UE:
+
+- Compliant to the corresponding specifications (e.g., 3GPP, TETRA).
+- For usage of the AT interface for trigger point measurements, the UE has to be conformant to the corresponding specifications, e.g., [ETSI TS 127 007] and [ETSI TS 127 005] for 3GPP and [ETSI EN 300 392-5] for TETRA.
+- Remote controllable to initiate the QoS tests with required parameter settings.
+- Delivering the necessary data which is required for the QoS test.
+- For benchmark tests only, UE with the same capabilities can be used. (e.g., max. number of timeslot (TS) allowed or best type of speech codec).
+- Depending on the test case, additional requirements may be relevant.
+
+## 9.2.5 Antennas
+
+Depending on the test case, the Test-UE's own antenna or an external antenna has to be used.
+
+Where applicable, the antennas have to be arranged in a well-defined fixed way with a minimum distance to each other reducing RF influence on an acceptable level in an equal radio environment. The coupling loss between 2 UEs should be minimum 40.5 dB (as specified in clause 2 of [ETSI TS 100 910]).
+
+NOTE 1 – Certain types of system integration will not allow satisfying these requirements, due to the limitation of space (e.g., backpack system). However, the antennas should be mounted on pre-determined points of the system which guarantee a minimum RF influence.
+
+External antennas are typically used for measurements in vehicles (car, train, ship). Two RF scenarios can be defined for vehicles:
+
+- 1) User with car kit and external antenna:
+ - Car mounted antenna with no extra attenuation has to be used.
+- 2) User without car kit, using the UE only (In-car use):
+ - Internal antenna of UE can be used.
+
+NOTE 2 – Simulation without body loss.
+
+or
+
+- External antenna connected with an overall attenuation of approximately 13 dB (cable loss plus extra attenuation) should be used.
+
+NOTE 3 – More information about antenna attenuation is to be found in [ETSI TR 102 581].
+
+## **9.2.6 Controller/processor/storage**
+
+The performance of the unit should be high enough and have no measurable impact on the correctness of the data collection. If the unit runs out of any resources, it shall inform the user on the MMI.
+
+## **9.2.7 Man machine interface (MMI)**
+
+### **9.2.7.1 Local controlled systems**
+
+The MMI has to allow full operation of the system by the operator. The main functionalities can be monitored and the operator is alerted in case of main failures. Some basic failure diagnostic is possible.
+
+### **9.2.7.2 Remote controlled systems**
+
+An MMI can be connected to perform basic tests and some failure diagnostics. Unattended systems should provide means to generate alarms upon operational faults in the system.
+
+## **9.2.8 Time sources**
+
+The clocks on the measurement systems have to be synchronized periodically. The required accuracy of the timestamps of the measurement item is:
+
+- Relative: 20 ms.
+- Absolute: 250 ms.
+
+## **9.2.9 Environmental conditions**
+
+The measurement system has to meet at least the minimal environmental conditions requirements defined in clauses 9.3.4 and 9.4.5.
+
+# **9.3 Fixed QoS test equipment (FQT)**
+
+## **9.3.1 General**
+
+Depending on the test scenarios defined in clause 9.1 of this Recommendation, there will be different requirements for the FQT and MQT.
+
+## **9.3.2 Controller**
+
+The performance of the unit should be high enough and have no measurable impact on the correctness of the data collection.
+
+## **9.3.3 Time-sources**
+
+See clause 9.2.8.
+
+## **9.3.4 Environmental conditions**
+
+The FQT has to meet the following environmental conditions:
+
+- Temperature: 5 °C to 40 °C.
+- Humidity: maximum 90 per cent.
+
+## **9.3.5 FQT for telephony measurements**
+
+### **9.3.5.1 Common aspects**
+
+For the user-to-user services different FQTs can be used. Also, the connection point to the network can vary between an ISDN or PSTN line and a direct link at the MSC/RNC.
+
+However, the type of server and the connection used have to be stored in the final measurement result. The topology of the distributed system (MQT and FQT) results in the necessity of merging the measurement results made on the FQT and on the MQT. The result will be merged on the time base. A unique identifier has to be included in the measurement files to enable identifying what files belong to the same measurements. This identifier has to be generated automatically by the measurement system.
+
+### 9.3.5.2 Telephony voice
+
+Calculating telephony QoS parameters including speech quality requires a counterpart on the fixed network. This is typically a PC which is connected to the PSTN. An application on the PC answers the incoming calls from the MQT or generates calls to the MQT.
+
+A FQT application for telephony voice service handles the CS connections of the voice calls, like an answering machine. It controls 1 to n mobile station numbers (MSNs). Each MSN has its own profile for incoming or outgoing calls. The FQT application has to have the following capabilities:
+
+- Auto answering of incoming calls.
+- Speech quality assessment of the incoming voice calls (uplink).
+- Providing speech samples for the downlink.
+- Generating MT calls.
+
+### 9.3.5.3 Telephony video
+
+For telephony video different types of connection points are available:
+
+- ISDN line/direct connection at MSC/RNC.
+- UMTS user equipment with video telephony application.
+- UMTS user equipment used as modem with a video telephony stack on the PC.
+
+A FQT application for telephony video handles the CS connections of the video calls. It controls 1 to n connection points. Each connection point has its own profile for the incoming calls. The FQT application has to have the following capabilities:
+
+- Auto answering of incoming video calls.
+- Providing speech/video samples for the downlink.
+
+### 9.3.6 FQT for store-and-forward services
+
+Store-and-forward services typically transports information between two user equipments. Therefore, the typical difference between MQT and FQT does not apply for store-and-forward services. The quality of store-and-forward services depends on one side on the RF coverage and on the other side, much more than other services, on the network internal infrastructure like the SMSC.
+
+The measurement system shall be able to measure all specified QoS parameters using user equipments (UEs) only.
+
+#### 9.3.6.1 Common aspects
+
+A general problem is that the A-party does not get a confirmation if the message was received successfully by the B-party. Also, the network internal infrastructure can change the order of messages. The B-party of the measurement system has to be able to handle this effect.
+
+### 9.3.7 FQT for data measurements
+
+The FQT is the measurement server as the physical machine plus the service application.
+
+It shall serve the service requests from the MQT in order to achieve the QoS parameter defined in clause 7. The server has to support the user profiles defined in clause 10.
+
+It has to be ensured that the highest possible throughput on the measurement server is higher than the expected throughput for the measurements.
+
+## **9.4 Mobile QoS test equipment (MQT)**
+
+### **9.4.1 General**
+
+Depending on the test scenarios defined in clause 9.1 of this Recommendation, there will be different requirements for the MQT.
+
+### **9.4.2 Controller**
+
+The performance of the unit should be high enough and have no measurable impact on the correctness of the data collection.
+
+### **9.4.3 Geographical positioning**
+
+Geographical data (position, speed, and heading) shall be collected during a mobile measurement. The geographical position can be retrieved by the following alternatives:
+
+- Geographical information shall be taken from general positioning system (GPS) whenever possible.
+- If no GPS signal is available other tools have to be used, e.g., navigation on a geo-referenced bitmap.
+
+#### **9.4.3.1 Format of geographical coordinates**
+
+- Outdoor: World geodetic system 1984 (WGS-84).
+- Indoor: Fixed reference points on a geographical referenced map and WGS-84 positions calculated by the software.
+
+#### **9.4.3.2 Accuracy**
+
+The accuracy of the geographical positioning has to meet the following requirements:
+
+- Outdoor: < 15 m.
+- Indoor: < 10 m.
+
+### **9.4.4 Time-sources**
+
+See clause 9.2.8.
+
+### **9.4.5 Environmental conditions**
+
+The FQT has to meet at least the following environmental conditions:
+
+- Temperature: 5 °C to 40 °C.
+- Humidity: maximum 90 per cent.
+
+Information about the necessary power supply has to be available.
+
+## **9.4.6 MQT for telephony measurements**
+
+### **9.4.6.1 Common aspects**
+
+For the user-to-user services different MQTs can be used. The topology of the distributed system (MQT and FQT) results in the necessity of merging the measurement results made on the MQT and on the FQT. The result will be merged on the time base.
+
+Due to the fact, that these systems will operate in moving vehicles or are carried around, they have to be ruggedly constructed. However, all local laws concerning industrial and/or road safety regulations have to be satisfied.
+
+The manufacturer shall provide a system manual, which shows the set-up of the system. For easy identification, elements of the system have to be labelled. Labels on all cables and connectors shall simplify the operation of the system.
+
+#### **9.4.6.2 Telephony voice**
+
+The MQT for voice has to provide software, which provides the means to generate the necessary calls or to answer automatically incoming calls from the FQT.
+
+The received speech sample is measured via the analogue output of the UE. As a consequence, the hardware set-up of the system has to include the necessary electrical adaptation between certain UEs and the controller (soundcard).
+
+The call generator of the MQT shall establish voice call as specified in clause 10.
+
+The hardware set-up of such a system is essential for the correctness of the measured QoS parameters. The system includes a UE specific part, which is responsible for the electrical adaptation of the audio output of the handset to the input of the soundcard of the controller.
+
+#### **9.4.6.3 Telephony video:**
+
+The MQT for video has to provide a call generator, which generates automatically calls as specified in clause 10 or to answer automatically incoming calls from the FQT. The system has to be capable of providing the following measurement methods.
+
+- Video telephony using the UE application; or
+- Video telephony using a video telephony stack on the PC.
+
+#### **9.4.7 MQT for store-and-forward services**
+
+See clause 9.3.6.
+
+#### **9.4.8 MQT for data measurements**
+
+##### **9.4.8.1 Common aspects**
+
+Data measurements require a client on the MQT side for the different applications. These clients can be either part of the MQT application or an external application remotely controlled by the MQT. However, the MQT has to log the type and the configuration of the client used for the measurement.
+
+##### **9.4.8.2 MQT for FTP**
+
+The used FTP client has to support the following points:
+
+- Active/passive mode.
+- Common firewall support.
+- Downloading from subdirectories.
+- Uploading to subdirectories.
+
+##### **9.4.8.3 MQT for e-mail**
+
+The e-mail client has to support POP3 and SMTP or IMAP for sending and receiving e-mails.
+
+##### **9.4.8.4 MQT for HTTP**
+
+The used HTTP client (browser) has to support all common HTTP versions. The client has to download an implemented static version of one of the ETSI reference webpages1. For details see [ETSI TR 102 505]. The used HTTP version and all other settings have to be logged.
+
+---
+
+1 Available at:
+
+#### **9.4.8.5 MQTT for WAP**
+
+The WAP client shall support all common WAP versions. The used WAP version has to be logged.
+
+#### **9.4.8.6 MQTT for streaming services**
+
+The MQTT has to support the required streaming clients.
+
+### **9.5 Mobile based measurement equipment**
+
+The functionalities of the MQTT as described in the clause 7 can also be realised on a single mobile phone. In such mobile based measurement equipment different types of QoS tests can run and test results can be logged for post processing. Mobile based measurement equipment may be controlled by an application, remotely or may be operated by a person.
+
+This type of equipment is for further study.
+
+## **10 Definition of typical measurement profiles**
+
+### **10.1 Measurement profiles**
+
+Measurement profiles are required to enable benchmarking of different networks both within and outside the national boundaries. It is necessary to have these profiles so that when a specific set of tests is carried out then users are comparing "like for like" performance.
+
+It is recognized that many factors will affect comparability:
+
+- number of sessions;
+- session durations;
+- time between sessions;
+- demanded QoS settings for data services;
+- protocol settings (like TCP/IP settings for data services or adaptive multi-rate (AMR) settings for speech services);
+- usage profile during the session;
+- fixed network test equipment like test servers for data sessions;
+- user profile stored in the HLR or the GPRS register (GR);
+- geographic location;
+- type of location (e.g., indoor, hotspot, city, suburban, rural, train, etc.);
+- speed when mobile;
+- type of vehicle;
+- type of antenna;
+- handset type;
+- handset hardware and firmware version;
+- service being tested and limitations of service;
+- network configuration;
+- mobile users' population density.
+
+For the points mentioned above where there is no recommendation or requirement in this Recommendation, the settings experienced by a regular user of the service under test in the network under test shall be used as a guideline.
+
+As far as possible, all particular values, e.g., timeout values are named preserving the name of the respective QoS parameters as defined in clause 7.
+
+### 10.1.1 Classification of measurement environments
+
+For interpretation and comparability of test results, it is important to know in which measurement environment the tests were performed. The environment classifications described in Tables 10-1 and 10-2 shall be used. Since the type of the measurement locations may be interpreted differently, the particular understanding of the location type determining a category shall be described in the results report.
+
+**Table 10-1 – Stationary tests**
+
+| Category | Location type | Additional information |
+|----------|----------------------------------------------------------------------------------------------|------------------------|
+| S10: | Airports, railway stations, shopping centres, malls, business districts and exhibition areas | Outdoor measurement |
+| S11: | Airports, railway stations, shopping centres, malls, business districts and exhibition areas | Indoor measurements |
+
+**Table 10-2 – Drive tests and walk tests**
+
+| Category | Location Type | Additional information |
+|----------|-----------------------------------|------------------------|
+| D1: | Train measurements | |
+| D2: | Urban areas (medium cities) | |
+| D3: | Highways | |
+| D4: | Rural areas (country roads) | |
+| D5: | Large cities | |
+| W1: | Walk tests (indoor measurements) | |
+| W2: | Walk tests (outdoor measurements) | |
+
+NOTE – Drive tests may be performed in the car using external antenna with an appropriate attenuation.
+
+### 10.1.2 Service profiles
+
+This clause describes recommended service profiles used for testing.
+
+#### 10.1.2.1 Telephony
+
+The service profiles defined for telephony might be applicable for different scenarios, e.g., mobile-to-mobile or mobile-to-fixed, and the respective results should not be compared directly, if so.
+
+To achieve comparable statistics when performing a benchmark, there should be no fixed pause between calls. Instead, a fixed call window is defined in which the call has to be performed. If the call fails or drops, the next call attempt shall only be made when the next call window arrives.
+
+A minimum pause interval between two call attempts should be applied to prevent network related problems between connection release and the next establishment (e.g., signalling in the packet switched data (PSD) or mobility management).
+
+##### 10.1.2.1.1 Speech telephony
+
+The following call durations (CDs) shall be used:
+
+- CD1: 10 seconds for call set-up testing;
+- CD2: 120 seconds for typical tests, default call duration;
+- CD3: 300 seconds for stability tests;
+
+- CD4: The call duration for, e.g., TETRA is for further study.
+
+**Call window:** Call duration + 30 seconds, (for the set-up and release phases) + 30 seconds (for the minimum pause interval), for the default call duration this results in 180 seconds.
+
+Timeout values:
+
+- Telephony {service non-accessibility | set-up time} timeout: 20 seconds.
+
+#### 10.1.2.1.2 Video telephony
+
+Video telephony should be tested in mobile-to-mobile scenarios. The following call durations shall be used:
+
+- CD1: 10 seconds for call set-up testing;
+- CD2: 120 seconds for typical tests (default call duration);
+- CD3: 300 seconds for stability tests.
+
+**Call window:** Call duration + 30 seconds (for the set-up and release phases) + 30 seconds (for minimum pause interval), for the default call duration this results in 180 seconds.
+
+Timeout values:
+
+- VT service {Non-accessibility | access time} timeout: 20 seconds;
+- VT audio or video set-up {failure ratio | time} timeout: 30 seconds.
+
+#### 10.1.2.1.3 Group call
+
+Group calls should be tested for the mobile-to-mobile(s) scenarios. The following call durations shall be used:
+
+- CD1: 20 seconds for typical tests (default call duration);
+- CD2: 60 seconds for stability tests.
+
+**Call window:** Call duration + 30 seconds (for the set-up and release phases), + 30 seconds (for minimum pause interval), for the default call duration this results in 80 seconds.
+
+Timeout values:
+
+- Group call service non-accessibility timeout: 5 seconds.
+
+#### 10.1.2.2 Messaging services
+
+For all messaging services it is important that the recipient of a message is not interrupted by the next message while retrieving the previous one. For this reason, it is important that the interval between sending two messages is larger than the 95th percentile of the end-to-end duration, unless measures are taken to avoid this kind of interference.
+
+It should be noted that mobility of either the sender of a message or the receiver of a message or both can have an impact on the results. Therefore, it is recommended that measurements are not only performed stationary, but also with mobility of one or both participants. In all cases, the used scenario has to be stated.
+
+##### 10.1.2.2.1 {SMS | SDS}
+
+{SMS | SDS} should be tested in mobile-to-mobile scenarios and without concatenation. Thus, the user data should be chosen in a way that it will fit into a single message.
+
+The interval between two consecutive SMS shall be 70 seconds.
+
+The transmission window of measurements shall be 175 seconds.
+
+Timeout-values:
+
+- {SMS | SDS} service non-accessibility timeout: 65 seconds;
+
+- {SMS | SDS} completion failure ratio timeout: 175 seconds;
+- {SMS | SDS} receive confirmation failure ratio timeout: for further study;
+- {SMS | SDS} consumed confirmation failure ratio timeout: for further study.
+
+#### 10.1.2.2.2 Concatenated {SMS | SDS}
+
+For further study.
+
+#### 10.1.2.2.3 MMS
+
+MMS should be tested end-to-end. That means, a MMS sent by the A-Party should be received by the B-Party using also a mobile phone. The advantage of this testing is that the MO direction at the A-Party and the MT direction at the B-Party can be measured. Both directions together are the end-to-end parameters described in clause 7.
+
+The following MMS sizes shall be used:
+
+- MMS1: 2 kbyte;
+- MMS2: 28 kbyte;
+- MMS3: 90 kbyte.
+
+If the MMS is not delivered at the destination after the MMS end-to-end failure ratio timeout, the MMS delivery is considered failed. MMS delivered after this time is not taken into account for end-to-end delay, but into end-to-end failure ratio.
+
+Timeouts for MMS over GPRS:
+
+The timeouts for MMS send, retrieval and end-to-end failure are dependent on the MMS size. For GPRS all MMS uploads with less than 5 kbits and all MMS downloads with less than 10 kbits are considered to be cut-off.
+
+MMS send failure ratio (MO) timeout: $(195 + \text{size [kbyte]} \times 8 \times 2/10)$ [seconds].
+
+MMS retrieval failure ratio (MT) timeout: $(195 + \text{size [kbyte]} \times 8 \times 1/10)$ [seconds].
+
+The fixed part of 195 seconds incorporate the time for PDP context activation and WAP activation and shall be used as a whole, i.e., the single timeouts for PDP context and WAP activation shall not be considered.
+
+MMS end-to-end delivery failure ratio timeout: $(590 + \text{size [kbyte]} \times 8 \times 2/10 + \text{size [kbyte]} \times 8 \times 1/10)$ [seconds].
+
+The fixed part of the 590 seconds incorporates the time for PDP context activations, WAP activations and notification and a security margin. It shall be regarded as a whole, i.e., the single timeouts shall not be considered.
+
+MMS notification failure ratio timeout: 120 seconds.
+
+Timeouts for MMS over UMTS:
+
+- The timeouts for MMS send, retrieval and end-to-end failure are dependent on the MMS size.
+- The respective required minimum upload and download data rate is for further study.
+- MMS send failure ratio (MO) timeout: for further study.
+- MMS retrieval failure ratio (MT) timeout: for further study.
+- MMS end-to-end delivery failure ratio timeout: for further study.
+- MMS notification failure ratio timeout: 120 seconds.
+
+### 10.1.2.3 Data services
+
+#### 10.1.2.3.1 Circuit switched
+
+Circuit switched data services shall be tested for 100 per cent of mobile originated calls (MOCs). Call duration shall be either 300 seconds or is defined by the usage profile used during the data session. The pause interval between call attempts shall be 30 seconds. The usage profile used during the data session is defined in clause 10.1.3.
+
+#### 10.1.2.3.2 Packet switched
+
+Packet switched data services shall be tested for 100 per cent of MOC sessions. Session duration shall be either 300 seconds or is defined by the usage profile used during the data session. The pause interval between session set-up attempts shall be 30 seconds. The usage profile used during the data session is defined in clause 10.1.3.
+
+NOTE – In order to ensure comparable results in benchmark testing (on changing access technologies), the number of measurements per time on the compared channels should be equal (by using test windows or regular intermediate results) or the individual measurements should be appropriately weighted in the aggregation.
+
+##### 10.1.2.3.2.1 Service-independent timeout values
+
+- Attach timeout: 75 seconds.
+
+It might occur that the user equipment sends more than one attach request towards the SGSN, since retries are necessary. A maximum of four retries are possible (timer T3310 expires after 15 seconds for each attempt, see [ETSI TS 124 008]).
+
+- PDP context activation timeout for GSM and 3G networks: 150 seconds.
+
+It might occur that the user equipment sends more than one PDP context activation request towards the SGSN, since retries are necessary. A maximum of four retries are possible (timer T3380 expires after 30 seconds for each attempt, see [ETSI TS 124 008]).
+
+- PDP context activation timeout for TETRA networks: 120 seconds.
+
+The PDP\_ACTIVATE\_WAIT timer expires after 30 seconds for each attempt, see clause 28.5.1.1 of [ETSI EN 300 392-2]. The number of possible retries RETRY\_ACTIVATION is fixed to 3, see clause 28.5.2 of [ETSI EN 300 392-2]. Therefore, the timeout interval for the PDP context activation procedure is 120 seconds, i.e., if the PDP context activation procedure was not completed after 120 seconds, it is considered as failure.
+
+##### 10.1.2.3.2.2 Service-dependent timeout values
+
+###### Timeout values for an FTP (UL and DL) service are:
+
+- Service accessibility timeout: 150 seconds + IP-service access timeout.
+- Set-up time timeout: 150 seconds + IP-service access timeout.
+- IP-service access timeout: 30 seconds.
+
+Data transfer cut-off timeout:
+
+- Over GPRS:
+ - UL: File size [kbyte] $\times$ 8 $\times$ 2/19;
+ - DL: File size [kbyte] $\times$ 8 $\times$ 1/10.
+- Over UMTS:
+ - UL and DL: File size [kbyte] $\times$ 8 $\times$ 1/50.
+- Dual mode: The average between the timeout over GPRS and UMTS shall be considered.
+
+**Timeout values for an HTTP service are:**
+
+- Service accessibility timeout: 150 seconds + IP-service access timeout.
+- Set-up time timeout: 150 seconds + IP-service access timeout.
+- IP-service access timeout: 30 seconds.
+- Data transfer cut-off timeout:
+ - Over GPRS:
+ - UL: File size [kbyte] $\times$ 8 $\times$ 2/10;
+ - DL: File size [kbyte] $\times$ 8 $\times$ 1/10.
+ - Over UMTS:
+ - UL and DL: File size [kbyte] $\times$ 8 $\times$ 1/50.
+ - Dual mode: The average between the timeout over GPRS and UMTS shall be considered.
+
+**Timeout values for an e-mail (IMAP, POP3 and SMTP) service are:**
+
+- Service accessibility timeout: 150 seconds + IP-service access timeout.
+- Set-up time timeout: 150 seconds + IP-service access timeout.
+- IP-service access timeout: 60 seconds.
+
+Data transfer cut-off timeout:
+
+- Over GPRS:
+ - UL: File size [kbyte] $\times$ 8 $\times$ 2/10;
+ - DL: File size [kbyte] $\times$ 8 $\times$ 1/10.
+- Over UMTS:
+ - UL and DL: File size [kbyte] $\times$ 8 $\times$ 1/50.
+- Dual mode: The average between the timeout over GPRS and UMTS shall be considered.
+
+**Timeout values for a streaming service are:**
+
+- Streaming service access timeout: 30 seconds.
+- Stream reproduction start timeout (initial buffering): 60 seconds.
+- Rebuffering timeout (single): 30 seconds.
+- Rebuffering timeout (total): 75 per cent of session time.
+
+NOTE 1 – It might occur that a streaming client goes from rebuffering back to playback within the rebuffering timeout (single), but goes back to one or more rebuffering periods afterwards. The rebuffering timeout (total) defines a limit in terms of a maximum of allocated time for all rebuffering periods.
+
+- Maximum allowed rebuffering frequency: 20 rebuf/min.
+
+NOTE 2 – The streaming client might go into recurrent rebufferings. If the number of rebuffering occurrences within a minute exceeds this limit the session is aborted.
+
+- Teardown timeout: 30 seconds.
+
+**10.1.3 Usage profiles for data sessions**
+
+For data session measurements, the client application, e.g., web browser, FTP client or mail client, as well as the server application, e.g., web server, FTP server or mail server, should behave in a way similar to the majority of client applications used by the user and server applications used by the data service providers.
+
+Also, the operating system on both sides, namely the client and the server side, should be chosen with respect to the operating system commonly used by the user and the data service provider, respectively.
+
+In case a network operator whose network is to be measured provides the user with some client application, it should be ensured that any change introduced by such application to the client operating system should have been applied prior to the measurement, as well. This is especially true for changes which would have an impact on the measurement results, for example changes to the operating system's TCP stack. Such client applications are for example provided in order to allow the user a single point of access to network related configurations and to data service clients, e.g., web browser, mail client, etc. Furthermore, such client application might optimize operating system parameters, including tuning of, e.g., TCP settings, with respect to the connection type and technology to be used.
+
+NOTE 1 – In some cases it is desirable not to install such client application itself since this might have some unwanted impact on the measurement. For example, if such application would generate unwanted network traffic in order to check for updates or if the application would continuously try to connect to the mobile device preventing some measurement application from controlling the device.
+
+NOTE 2 – The use of different operating systems as well as the use of operating systems with different TCP parameter settings in general might have a large impact on the results obtained. With respect to the operating systems, this is due to the different implementations of the TCP/IP stack. This needs to be considered in the case of benchmarking exercises where the client and/or server operating systems and/or the changes applied on the client sides of the compared networks are not the same. However, the settings of the TCP stack of both the client and the server operating system should be recorded in order to allow for better interpretation and in-depth evaluation of the measurement data.
+
+NOTE 3 – Proxy servers installed in the networks IP core network may act as the TCP peer instead of the application server the tests are performed against. In benchmarking scenarios, the existence of different proxy servers might have an additional impact on the results, which should be considered when comparing them.
+
+NOTE 4 – A reference for optimized TCP parameters over cellular networks, like the second-generation (2.5G) and the third-generation (3G) wireless networks, is [IETF RFC 3481].
+
+NOTE 5 – Some of the client applications referred to above might also change the way a data service is accessed from the client side, for example by introducing some client to the user's operating system which changes the transport protocol between the user's operating system and some proxy server. In such case, the trigger points as defined in clause 7 might not be measurable anymore and should therefore be mapped to the application layer. Especially, in case of benchmarking exercises where different operating systems are used on the client side, such mapping might have an additional influence on the measurements. The definition of such trigger point mappings for the different operations systems is not in the scope of this Recommendation.
+
+For all tests, a dedicated test server should be used as a well-defined reference. Under no circumstances should a commercial server be used, since the content on such a server may change over time. This makes future reproduction of the results impossible.
+
+In order to avoid issues with DNS host name resolution like including effects of DNS caching strategies of the used operating system into the measurement, the test server should either be identified by an IP address and not by its fully qualified domain name (FQDN) or it shall be ensured that the local resolver will contact a remote DNS name server in case a host name resolution is requested by an application. Furthermore, the DNS name server should be able to perform the resolution within its local zone, in case DNS lookup time is to be included into any quality of service parameter to get calculated. The latter is needed in order to exclude effects of DNS caching strategies of the DNS name server(s) involved into the measurement.
+
+The measurement of data services should take place against a reference server only used for testing purposes. The reference server should be connected to the public Internet with a link capable of carrying the accumulated traffic of all mobiles testing against that server (e.g., if a benchmark with 4 networks is performed, the server should be able to deliver at least 4 times the maximum nominal speed of a single wireless link). There should be no bias concerning the IP connectivity to this server from a specific operator (e.g., bandwidth or hop-count).
+
+The capabilities of the test UE shall be stated in the results report.
+
+### 10.1.3.1 Web browsing using HTTP
+
+For the measurement of web browsing, the reference server should contain a static version of one of the ETSI reference webpages2. For details see [ETSI TR 102 505].
+
+The browser used for testing should behave in a way similar to the browser used by most of the users. It should be able to support the same HTTP capabilities and headers and open the same number of parallel download threads to download the content as the reference browser.
+
+After one test cycle (one download of the reference page), the complete data representation of the reference page content shall be cleared from the local cache of the browser. Furthermore, it should be made sure, that all TCP connections between the server and the client are closed (i.e., no HTTP kept alive). There should be a pause of at least 6 seconds between the cycles.
+
+NOTE – Any data related to performance enhancement proxy (PEP) settings, like JavaScript scripts or Cookies need to be prevented from getting cleared from the browser's cache.
+
+For the test, only HTTP download should be used. HTTP upload shall not be used.
+
+Testing of content integrity is not mandatory for this test, but highly recommended.
+
+### 10.1.3.2 E-mail access
+
+E-mail access should take place against a reference mail server.
+
+For the measurement of e-mail, services reference content should be used.
+
+A reference e-mail shall have a body containing only the string "Test" and attachment(s) chosen from the following reference content building blocks with respect to expected data rates of the network under test.
+
+Available building blocks are (files to be used as attachments): 100 kbyte, 200 kbyte, 500 kbyte, 1 Mbyte, 2 Mbyte, 5 Mbyte and 10 Mbyte.
+
+NOTE – See also the table in Appendix VIII for typical upload or download times versus file size and used data rate.
+
+It is recommended to use the binary files provided by ETSI3; they contain random data in order to exclude optimizer or accelerator effects.
+
+A cycle should consist of mail upload using SMTP and mail download using IMAP or POP3. Both upload and download should represent typical user behaviour.
+
+After a test cycle, all TCP connections to the server should be disconnected.
+
+Testing of content integrity is mandatory for this test.
+
+### 10.1.3.3 File transfer using FTP
+
+FTP testing should take place against a reference FTP server. The server should support the standard FTP commands and both active and passive mode transfer of data. There should be no bandwidth limitation on application level.
+
+In case of multisession scenarios, the reader shall be aware of the resulting effects with respect to the QoS parameters measured for each single session. With that, any use of multisession scenarios shall be stated in the results report.
+
+In case of downloading a chunked single file via multiple data connections simultaneously, one shall be aware of the resulting effects with respect to the QoS parameters measured. With that, any use of
+
+2 Available at:
+
+3
+
+a simultaneous download of a chunked single file via multiple data connections as well as the number of chunks used to transfer the file during this session shall be stated in the results report.
+
+#### **10.1.3.4 File sharing using UDP**
+
+To be decided.
+
+#### **10.1.3.5 Synthetic tests**
+
+##### **10.1.3.5.1 UDP**
+
+For further study.
+
+##### **10.1.3.5.2 ICMP**
+
+For further study.
+
+##### **10.1.3.5.3 TCP**
+
+For further study.
+
+### **11 Post processing and statistical methods**
+
+#### **11.1 Important measurement data types in mobile communications**
+
+Appropriate data analysis methods should depend on the type of the given data as well as on the scope of the analysis. Therefore, before analysis methods are described, different data types are introduced and differences between them are pointed out.
+
+Four general categories of measurement results are expected when QoS measurements are done in mobile communications.
+
+##### **11.1.1 Data with binary values**
+
+Single measurements related to the topics:
+
+- service accessibility, service availability;
+- service retainability, service continuity;
+- error ratios, error probabilities;
+
+in general show a binary outcome, i.e., only two outcomes are possible. This means the result of a single trial leads to a result which is either valued positive or negative related to the considered objective. The result may be recorded as decision-results Yes/No or True/False or with numerical values 0 = successful and 1 = unsuccessful (i.e., errors occur) or vice versa. Aggregation of trials of both types allows to calculate related ratios which means the number of positive/negative results is divided by the number of all trials. Usually, the units of numerator and denominator are the same, namely number of trials.
+
+Example: If established voice calls are considered to test the service retainability of a voice telephony system, every successfully completed call leads to the positive result "Call completed", every unsuccessfully ended call is noticed as "Dropped call" which represents the negative outcome. After 10 000 established calls, the ratio of dropped calls related to all established calls can be calculated. The result is the call drop probability.
+
+##### **11.1.2 Data out of time-interval measurements**
+
+Measurements related to the time domain occur in the areas:
+
+- duration of a session or call;
+- service access delay;
+
+- round trip time and end-to-end delay of a service;
+- blocking times, downtimes of a system.
+
+The outcome of such measurements is the time span between two time stamps marking the starting and the end point of the time periods of interest. Results are related to the unit "second" or multiples or parts of it. Depending on the measurement tools and the precision needed, arbitrarily small measurement units may be realized.
+
+Example: Someone can define the end-to-end delivery time for the MMS service by a measurement which starts when the user at the A party pushes the "send" button and which stops when the completely received MMS is signalled to the user at the B party.
+
+### 11.1.3 Measurement of data throughput
+
+Measurements related to data throughput result in values which describe the ratio of transmitted data volume related to the required portion of time. The outcome of a single measurement is the quotient of both measures. Used units are "bit" or multiples thereof for the data amount and "second" or multiples or parts thereof for the portion of time.
+
+Example: If a data amount of 1 Mbit is transmitted within a period of 60 seconds, this would result in a mean data rate of approximately 16.66 kbit/s.
+
+### 11.1.4 Data concerning quality measures
+
+Examples are given by the quality of data transfer which may be measured by its speed or evaluations of speech quality measured on a scale, respectively.
+
+Measurements related to audio-visual quality can be done objectively by algorithms or subjectively by human listeners. The outcome of audio-visual quality evaluation is related to a scaled value which is called mean opinion score (MOS) for subjective testing. Thereby, two types of quality measurement are distinguished; subjective and objective measurements. If quantitative measures are identified which are highly correlated to the quality of interest, this will simplify the analysis. However, if this is not possible, some kind of evaluation on a standardized scale by qualified experts is needed. The result may therefore be given either as the measurement result or as a mark on a pre-defined scale.
+
+Example: Within a subjective test, people are asked to rate the overall quality of video samples which are presented to them. The allowed scale to rate the quality is defined in the range from 1 (very poor quality) to 5 (brilliant quality).
+
+Table 11-1 summarizes the different kinds of QoS related measurements, typical outcomes and some examples.
+
+**Table 11-1 – QoS related measurements, typical outcomes and examples**
+
+| Category | Relevant measurement types | Examples |
+|------------------------|--------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------|
+| Binary values | Service accessibility, service availability
Service retainability, service continuity
Error ratios, error probabilities | Service accessibility telephony, service non-availability SMS
Call completion rate, call drop rate
Call set-up error rate |
+| Duration values | Duration of a session or call
Service access delay
Round trip time, end-to-end delay
Blocking times, system downtimes | Mean call duration
Service access delay WAP
ICMP ping roundtrip time
Blocking time telephony, SGSN downtime |
+| Throughput values | Throughput | Mean data rate GPRS
Peak data rate UMTS |
+| Content quality values | Audio-visual quality | MOS scores out of subjective testing |
+
+## 11.2 Distributions and moments
+
+### 11.2.1 Introduction
+
+The objective of data analyses is to draw conclusions about the state of a process based on a given data set, which may or may not be a sample of the population of interest. If distributions are assumed, these specify the shape of the data mass up to the parameters associated with each family of distributions specifying properties like the mean of the data mass. Location or dispersion shifts of the process will in general result in different parameter estimates specifying the distribution. Therefore, the information available from the data is compressed into one or few sufficient statistics specifying the underlying distribution.
+
+Many statistical applications and computations rely in some sense on distributional assumptions, which are not always explicitly stated. Results of statistical measures are often only sensible if underlying assumptions are met and therefore only interpretable if users know about these assumptions.
+
+This clause is organized as follows. First, distributions, moments and quantiles are introduced in theory in clauses 11.2.2 to 11.2.4. This part of the Recommendation is based on the idea of random variables having certain distributions. Random variables do not take single values but describe the underlying probability model of a random process. They are commonly denoted by:
+
+$$X \sim \text{distribution (parameters)}$$
+
+From the distributional assumptions, moments and quantiles of random variables are derived in theory.
+
+Data is often viewed as being the realizations of random variables. Therefore, data analysis mainly consists of fitting an appropriate distribution to the data and drawing conclusions based on this assumption. Clause 11.2.5 briefly summarizes the estimation of moments and quantiles.
+
+Subsequently, a number of important distributions are introduced in clause 11.2.6, each of which is visualized graphically to give an idea of meaningful applications. Within this clause, testing distributions are also introduced as they are needed in clause 11.2.7 for the derivation of statistical tests.
+
+### 11.2.2 Continuous and discrete distributions
+
+The main difference between the data types described above can be explained in terms of continuous and discrete distributions. Data with binary values follow a discrete distribution, since the probability mass is distributed only over a fixed number of possible values. The same holds for quality measurements with evaluation results on a scale with a limited number of possible values (i.e., marks 1 to 6 or similar).
+
+On the contrary, time-interval measurements as well as quality measurements based on appropriate quantitative variables may take an infinitely large number of possible values. In theory, since the number of possible outcomes equals infinity, the probability that a single value is exactly realized is zero. Probabilities greater than zero, are only realized for intervals with positive width. In practice, each measurement tool will only allow a limited precision resulting in discrete measurements with a large number of possible outcomes. Nevertheless, data from measurement systems with reasonable precision are treated as being continuous.
+
+Formal definitions for continuous and discrete distributions are based on probability density functions as is described in the following.
+
+### 11.2.3 Definition of density function and distribution function
+
+#### 11.2.3.1 Probability density function (PDF)
+
+Probability density functions (PDFs) specify the probability mass either for single outcomes (discrete distributions) or for intervals (continuous distributions).
+
+A PDF is defined as a function $f: \mathbb{R} \rightarrow [0, \infty]$ with properties:
+
+- i) $f(x) \geq 0$ for all $x \in S$ .
+- ii) $\int_S f(x) dx = 1$ for continuous distributions or $\sum_S f(x) = 1$ for discrete distributions.
+
+In other words, the values of the PDF are always non-negative, meaning that negative probabilities are neither assigned to values nor intervals, also the summation or integration over the PDF always results in 1 (= 100 per cent), meaning that any data value will always be realized.
+
+Example 1: A PDF for binary data may be given by $f(x) = \begin{cases} 0,1: x=1 \\ 0,9: x=0 \end{cases}$ , which implies that the probability for a faulty trial ( $x=1$ ) is 10 per cent, while tests are completed successfully with probability 90 per cent.
+
+Example 2: For time-interval measurements PDFs may take any kind of shape, as an example a normal distribution with mean 10 (seconds) is assumed here. The PDF for this distribution is given by $f(x) = \frac{1}{\sqrt{2\pi}} \exp\left\{-\frac{1}{2}(x-10)^2\right\}$ . Other examples for continuous distributions will follow later on.
+
+Example 3: If for instance categories for speech quality are defined as 1 = very poor up to 5 = brilliant, a PDF for the resulting data may be given by $f(x) = \begin{cases} 0,1 : x \in \{1,2,3\} \\ 0,4 : x = 4 \\ 0,3 : x = 5 \end{cases}$ .
+
+Figure 11-1 summarizes all three assumed example PDFs for different data types.
+
+
+
+The figure displays three separate plots, each representing a different type of probability density function (PDF).
+
+- Example 1:** A discrete PDF for binary data. The x-axis ranges from -1.0 to 2.0, and the y-axis (f(x)) ranges from 0.0 to 1.0. There are two vertical red lines (spikes): one at x=0 with a height of 0.9, and another at x=1 with a height of 0.1.
+- Example 2:** A continuous PDF representing a normal distribution. The x-axis ranges from 6 to 14, and the y-axis (f(x)) ranges from 0.0 to 0.4. The curve is a symmetric bell shape centered at x=10, with a peak height of approximately 0.4.
+- Example 3:** A discrete PDF for speech quality categories. The x-axis ranges from 1 to 5, and the y-axis (f(x)) ranges from 0.0 to 0.4. There are five vertical red lines: at x=1, 2, and 3 with heights of 0.1; at x=4 with a height of 0.4; and at x=5 with a height of 0.3.
+
+Figure 11-1 shows three plots of Probability Density Functions (PDFs). Example 1 is a discrete distribution with two spikes at x=0 (height 0.9) and x=1 (height 0.1). Example 2 is a continuous normal distribution centered at x=10. Example 3 is a discrete distribution with spikes at x=1, 2, 3 (height 0.1), x=4 (height 0.4), and x=5 (height 0.3).
+
+Figure 11-1 – Probability density functions (PDFs) of examples 1 to 3
+
+#### 11.2.3.2 Cumulative distribution function (CDF)
+
+A cumulative distribution (or density) function (CDF) is computed from the corresponding PDF as described before by summing (discrete) or integrating (continuous) over the density mass up to the current value.
+
+A function $F: \mathbb{R} \rightarrow [0,1]$ with $F(x) = \sum_{\tilde{x} \leq x} f(\tilde{x})$ for discrete and $F(x) = \int_{-\infty}^x f(\tilde{x}) d\tilde{x}$ for continuous distributions is called CDF. This implies $F(x) \rightarrow 1$ for $x \rightarrow \infty$ and $F(x) \rightarrow 0$ for $x \rightarrow -\infty$ .
+
+In other words, the value of the CDF corresponds to the proportion of the distribution left of the value of interest. For the three examples from above, the CDFs are given in Figure 11-2.
+
+
+
+The figure displays three plots of Cumulative Distribution Functions (CDFs):
+
+- Example 1:** A discrete CDF. The x-axis ranges from -1.0 to 2.0, and the y-axis (F(x)) ranges from 0.0 to 1.0. There are jumps at x=0 (to approximately 0.1) and x=1 (to 1.0).
+- Example 2:** A continuous CDF. The x-axis ranges from 6 to 14, and the y-axis (F(x)) ranges from 0.0 to 1.0. The curve is S-shaped, starting near 0 at x=6 and reaching 1.0 around x=12.
+- Example 3:** A discrete CDF. The x-axis ranges from 1 to 5, and the y-axis (F(x)) ranges from 0.0 to 1.0. There are jumps at x=1, 2, 3, 4, and 5, with the final value reaching 1.0 at x=5.
+
+Figure 11-2 shows three cumulative distribution functions (CDFs) for different examples. Example 1 is a discrete CDF with jumps at x=0 and x=1. Example 2 is a continuous, smooth CDF curve. Example 3 is a discrete CDF with jumps at x=1, 2, 3, 4, and 5.
+
+Figure 11-2 – Cumulative distribution functions (CDFs) of examples 1 to 3
+
+### 11.2.4 Moments and quantiles
+
+Moments are main characteristics of distributions. The most important moments are:
+
+- the **expected value** (first moment), specifying the location of the distribution;
+- the **variance** (second central moment), specifying the dispersion around the expected value of the distribution; and
+- the **skewness** (third central moment), specifying whether a distribution is symmetric or skewed.
+
+These moments are defined as follows.
+
+- The expected value (first moment, mean) of a random variable $x$ with CDF $f(x)$ is defined as $E(x) = \int x \cdot f(x) dx$ for continuous distributions or $E(x) = \sum x \cdot f(x)$ for discrete distributions, respectively.
+- The variance (second central moment) of a random variable $x$ with CDF $f(x)$ is defined as $Var(x) = \int (x - E(x))^2 \cdot f(x) dx$ for continuous distributions or $Var(x) = \sum (x - E(x))^2 \cdot f(x)$ for discrete distributions, respectively. The square root of the variance called standard deviation, denoted as $\sigma(x)$ , is often more informative since it is defined on the original data scale.
+- The skewness (third central moment) of a random variable $x$ with CDF $f(x)$ is defined as $\int (x - E(x))^3 \cdot f(x) dx$ for continuous distributions or $\sum (x - E(x))^3 \cdot f(x)$ for discrete distributions, respectively. A value of zero indicates a symmetric distribution.
+
+Example 1: For the CDF from example 1 the moments are given by $E(x) = 0.1 \times 1 + 0.9 \times 0 = 0.1$ , $Var(x) = 0.1 \times 0.9^2 + 0.9 \times 0.1^2 = 0.09$ resulting in a standard deviation $\sigma(x) = 0.3$ . The skewness can be computed as $0.1 \times 0.9^3 + 0.9 \times (-0.1)^3 = 0.072$ indicating that the distribution is not symmetric.
+
+Example 2: The moments of the above normal distribution can be computed by partial integration and the fact that the PDF integrates to 1, or by utilizing the properties of normal distributions stating that the mean and standard deviation are the parameters $\mu$ and $\sigma$ of the PDF $f(x | \mu, \sigma) = \frac{1}{\sigma\sqrt{2\pi}} \exp\left\{-\frac{1}{2\sigma^2}(x - 10)^2\right\}$ and that normal distributions are always symmetric. This results in $E(x) = 10$ , $Var(x) = \sigma^2 = 1$ , which also equals the standard deviation and skewness = 0 for the above example.
+
+Example 3: For the CDF of example 3, moments are computed by $E(x) = 0.1 \times 1 + 0.1 \times 2 + 0.1 \times 3 + 0.4 \times 4 + 0.3 \times 5 = 3.7$ , $Var(x) = 1.61$ and negative skewness of $-1.824$ .
+
+The moments are computable for all three example PDFs. Nevertheless, they are not always meaningful. In particular in the third example, the possible outcomes are "very poor" to "brilliant", which may be ordered and named 1 to 5 as has been done before, but the expected value of 3.7 does not have a strict meaning. The same applies for higher moments, since the values of the variable of interest are not quantitative, but ordered qualitative.
+
+In case of non-symmetric distributed data, moments may not be appropriate for describing the distribution of interest. An alternative measure of location is given by the **median**, which can be viewed as the point cutting the distribution into halves, namely 50 per cent of the distribution mass are smaller and 50 per cent are larger than the median.
+
+More generally, quantiles are defined for each possible percentage. The $\alpha$ -**quantile** cuts the distribution in a part of $\alpha \times 100\%$ of the distribution smaller than this value and $(1-\alpha) \times 100\%$ larger than this value. The median as a special case is also called 50%-quantile.
+
+A **formal definition of quantiles** is given in the following:
+
+- "The $\alpha$ -quantile $q_\alpha$ with $\alpha \in [0, 1]$ is defined as the smallest number $q_\alpha$ satisfying $F(q_\alpha) \leq \alpha$ (for $\alpha = 0$ , the minimum value with positive probability or $-\infty$ is defined, respectively)".
+
+Quantiles, shown in Figure 11-3, are easiest to illustrate and to compare with the examples of CDFs given above. For each CDF, the 5%, 50% and 75%-quantiles are added to the corresponding plot.
+
+
+
+Figure 11-3: Illustration of theoretical quantiles for examples 1 to 3. The figure contains three subplots labeled 'Example 1', 'Example 2', and 'Example 3'. Each plot shows a Cumulative Distribution Function (CDF) F(x) on the y-axis (ranging from 0.05 to 0.75) against x on the x-axis. The 5%, 50%, and 75% quantiles are marked on the x-axis as q\_0.05, q\_0.5, and q\_0.75 respectively. In Example 1, the CDF is a step function. In Example 2, the CDF is a smooth, S-shaped curve. In Example 3, the CDF is a step function with more steps than Example 1. Red horizontal lines indicate the quantile levels on the CDF curves.
+
+**Figure 11-3 – Illustration of theoretical quantiles for examples 1 to 3**
+
+### 11.2.5 Estimation of moments and quantiles
+
+If only samples from the population of interest are available, theoretical moments may not be computed, but have to be estimated empirically.
+
+A sample-based estimator of the expectation of the underlying distribution is given by the **empirical mean**
+
+$\bar{x} = \frac{1}{n} \sum_{i=1}^n x_i$ , where $x_i, i=1, \dots, n$ are the sample values. The **variance** of a distribution is
+
+commonly estimated by $s^2 = \frac{1}{n-1} \sum_{i=1}^n (x_i - \bar{x})^2$ with resulting **empirical standard deviation**
+
+$$s = \sqrt{\frac{1}{n-1} \sum_{i=1}^n (x_i - \bar{x})^2}.$$
+
+For estimating quantiles, the above definition of theoretical quantiles is commonly replaced by a linear interpolating function. This function on one hand ensures that all quantiles are realized within
+
+the range of the empirical distribution (0%-quantile equals the minimum of the data, 100%-quantile equals the maximum of the data). The interpolation on the other hand allows a "better guess" of the real quantile if only few data are given and the underlying distribution is continuous. The commonly used computation formula is given by:
+
+$$q_\alpha = (1 - f)x_{(i)} + f \cdot x_{(i+1)}$$
+
+where:
+
+$$i = \lfloor 1 + (n - 1) \cdot \alpha \rfloor, f = 1 + (n - 1) \cdot \alpha - i \text{ and}$$
+
+$$x_{(n+1)} := x_{(n)}.$$
+
+Here $x_{(i)}$ denotes the $i$ -th ordered data value and $\lfloor z \rfloor$ denotes the largest integer less or equal to $z$ , i.e., $\lfloor 3,2 \rfloor = 3, \lfloor 4,9 \rfloor = 4$ . Therefore, with the computation of $i$ , the quantile is localized depending on the value of $\alpha$ between $x_{(i)}$ and $x_{(i+1)}$ . The interpolation between these two values is done according to the deviation $f$ between $i$ and $(1 + (n - 1) \times \alpha)$ .
+
+Examples of empirical CDFs and empirical quantiles for data simulated from the example distributions 1 to 3 are given in Figure 11-4. The solid black line represents the empirical quantiles derived by the above formula (from 0 to 100 per cent).
+
+
+
+Figure 11-4a: Three plots showing empirical CDFs and quantiles for Example 1, 2, and 3 with n = 20. Each plot shows empirical F(x) on the y-axis (0.0 to 1.0) and x on the x-axis. A solid black line represents the empirical quantiles, and red dots represent the empirical CDFs. Example 1 shows a step function with a jump at x=1. Example 2 shows a smooth, S-shaped curve. Example 3 shows a step function with multiple jumps.
+
+Figure 11-4a
+
+
+
+Figure 11-4b: Three plots showing empirical CDFs and quantiles for Example 1, 2, and 3 with n = 1000. Each plot shows empirical F(x) on the y-axis (0.0 to 1.0) and x on the x-axis. A solid black line represents the empirical quantiles, and red dots represent the empirical CDFs. Example 1 shows a step function with a jump at x=1. Example 2 shows a smooth, S-shaped curve. Example 3 shows a step function with multiple jumps.
+
+Figure 11-4b
+
+Figure 11-4 – Illustration of empirical CDFs and quantiles for examples 1 to 3
+
+Note that the above estimation procedure should be applied with great care for data sets with only few data values where the underlying distribution is presumably discrete, since the estimated
+
+quantiles also take values differing from those contained in the given data set. This can also be seen from Figure 11-4a in the plots for samples with sample size $n = 20$ .
+
+### 11.2.6 Important distributions
+
+In this clause some of the important distributions related to practical usage in telecommunications are described. Either the mentioned distributions are directly related to measurement results or they are necessary to evaluate these results in a second step. Further relevant distributions may be appended later.
+
+In general, distributions are specified by certain parameters which describe their main characteristics. Commonly, the characteristics are expressed in terms of their moments, i.e., mean value and standard deviation or variance, respectively. Wherever possible, the relevant characteristics are given as well as examples of possible use-cases. In general, continuous and discrete distributions are distinguished further on.
+
+#### 11.2.6.1 Continuous distributions
+
+A large number of different continuous distributions are available to describe measurement results in a statistical manner. An overview is for instance given by [b-Law] or [b-Hartung]. For practical purposes in the field of quality of service (QoS) probing, the distributions described below are probably the most relevant ones.
+
+##### 11.2.6.1.1 Normal distribution
+
+The normal distribution, also called Gaussian distribution (or bell-shaped distribution) is used for many natural processes whenever a symmetric continuous distribution seems appropriate. (An example was given before and density functions of further normal distributions are given in Figure 11-5.)
+
+| | Normal distribution |
+|----------------|-----------------------------------------------------------------------------------------------------------|
+| Notation | $X \sim N(\mu, \sigma^2)$ |
+| Parameters | $\mu, \sigma$ |
+| PDF | $f(x) = \frac{1}{\sigma\sqrt{2\pi}} \exp\left\{-\frac{1}{2\sigma^2}(x-\mu)^2\right\}$ |
+| CDF | $F(x) = \int_{-\infty}^x \frac{1}{\sigma\sqrt{2\pi}} \exp\left\{-\frac{1}{2\sigma^2}(t-\mu)^2\right\} dt$ |
+| Expected value | $E(X) = \mu$ |
+| Variance | $Var(X) = \sigma^2$ |
+| Remarks | Standard normal distribution with $\mu = 0$ and $\sigma = 1$ , see clause 11.2.6.1.1.1 |
+
+The normal distribution is uniquely specified by its mean and standard deviation. For normally distributed data, about 68 per cent of the data are realized within the interval $[\mu - \sigma, \mu + \sigma]$ , 95 per cent are realized within $[\mu - 2\sigma, \mu + 2\sigma]$ and 99.7 per cent are realized within $[\mu - 3\sigma, \mu + 3\sigma]$ . The last interval is also called $6\sigma$ -interval which gave the name to the popular "Six-sigma"-courses.
+
+![Three plots of Normal PDFs. Example 1: m=100, s=20, showing 68% within [m-s, m+s]. Example 2: m=20, s=3, showing 95% within [m-2s, m+2s]. Example 3: Standard Normal with m=0, s=1, showing 99.7% within [m-3s, m+3s].](979bdcda78f8caee47f8255d871a9f43_img.jpg)
+
+Three plots of Normal PDFs. Example 1: m=100, s=20, showing 68% within [m-s, m+s]. Example 2: m=20, s=3, showing 95% within [m-2s, m+2s]. Example 3: Standard Normal with m=0, s=1, showing 99.7% within [m-3s, m+3s].
+
+**Figure 11-5 – Density functions of three different normal distributions**
+
+Normally (or nearly normally) distributed data is found quite often in practice, in particular in nature, for example human or animal body heights.
+
+**11.2.6.1.1.1 Standard normal distribution**
+
+All normal distributions or normally distributed data can be standardized by subtracting the mean and afterwards dividing by the standard deviation of the distribution or data resulting in a standard normal distribution with mean $\mu = 0$ and standard deviation $\sigma = 1$ . The inverse computation leads back to the original distribution or data. Therefore, all normal distributions may be reduced to the standard normal, if the parameters $\mu$ and $\sigma$ are known or estimated. Because of this and the fact that many statistical tests are based on the normal distribution, statistical textbooks often provide the quantiles of the standard normal distribution. In particular, the $\alpha$ -quantile of the standard normal distribution is denoted as $u_\alpha$ .
+
+In example 3 of Figure 11-5, the density of the standard normal distribution is given.
+
+| Standard normal distribution | |
+|-------------------------------------|---------------------------------------------------------------------------------------|
+| Notation | $X \sim N(0,1)$ |
+| Parameters | none |
+| PDF | $f(x) = \frac{1}{\sqrt{2\pi}} \exp\left\{-\frac{1}{2}x^2\right\}$ |
+| CDF | $F(x) = \int_{-\infty}^x \frac{1}{\sqrt{2\pi}} \exp\left\{-\frac{1}{2}t^2\right\} dt$ |
+| Expected value | $E(X) = 0$ |
+| Variance | $Var(X) = 1$ |
+| Remarks | |
+
+**11.2.6.1.1.2 Central limit theorem**
+
+Another reason for the frequent use of normal distributions (in particular for testing purposes) is given by the central limit theorem, one of the most important theorems in statistical theory. It states that the mean of $n$ equally distributed random variables with mean $\mu$ and variance $\sigma^2$ approaches a normal distribution with mean $\mu$ and variance $\sigma^2/n$ as $n$ becomes larger. This holds for arbitrary distributions and commonly the typical shape of the normal distribution is sufficiently reached for $n \geq 4$ . For further details about the central limit theorem see [b-Law] or [b-Mood] (see bibliography).
+
+A number of tools was developed for checking whether data (or means) are normal, namely test procedures like the well-known Kolmogorov-Smirnov goodness-of-fit test (see clause 11.2.6.6.1.2) among others or graphical tools like histograms or QQ-plots. The mentioned graphical tools will be introduced in clause 11.3.
+
+### 11.2.6.1.1.3 Transformation to normality
+
+As has been seen, the normal distribution is very powerful and can be applied in many situations. Nevertheless, it is not always appropriate, in particular in technical applications, where many parameters of interest have non-symmetric distributions. However, in these situations it may be possible to transform the data to normality. This idea leads for instance to the Log-Normal distribution, which is often assumed for technical parameters.
+
+### 11.2.6.1.2 Log-Normal distribution
+
+The distribution of a random variable is said to be log-normal, if the logged random variable is normally distributed, which is denoted by $\log(x) \sim N(\mu, \sigma^2)$ .
+
+| | Log-normal distribution |
+|----------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| Notation | $X \sim LN(\mu, \sigma^2)$ or $\log(X) \sim N(\mu, \sigma^2)$ |
+| Parameters | $\mu, \sigma$ |
+| PDF | $f_x(x) = \begin{cases} \frac{1}{\sigma x \sqrt{2\pi}} \exp\left\{-\frac{(\ln(x) - \mu)^2}{2\sigma^2}\right\} & \text{if } x > 0 \\ 0 & \text{else} \end{cases}$ |
+| CDF | $F(x) = \int_{-\infty}^x \frac{1}{\sigma \sqrt{2\pi}} \exp\left\{-\frac{1}{2\sigma^2}(t - \mu)^2\right\} dt$ |
+| Expected value | $E(x) = \exp\left(\mu + \frac{1}{2}\sigma^2\right)$ |
+| Variance | $Var(x) = \exp(2\mu + \sigma^2)(\exp(\sigma^2) - 1)$ |
+| Remarks | |
+
+Log-normal distributions are skewed and have heavier upper tails compared to the normal distribution implying a higher variability in the upper quantiles. Density examples for different values of $\mu$ and $\sigma$ are given in Figure 11-6 and illustrate that the log-normal distribution can take a variety of different shapes.
+
+
+
+The figure displays three separate plots of log-normal density functions. Each plot has a title and a subtitle indicating the parameters of the underlying normal distribution for the logarithm of the variable. Plot 1 (left) is for a standard normal log-distribution (μ=0, σ=1), with its peak at x=1 and a y-axis scale from 0.0 to 0.6. Plot 2 (middle) is for a normal log-distribution with μ=2 and σ=0.5, with its peak at x=e² ≈ 7.4 and a y-axis scale from 0.00 to 0.10. Plot 3 (right) is for a normal log-distribution with μ=2 and σ=1.5, with its peak at x=e² ≈ 7.4 and a y-axis scale from 0.00 to 0.10. All three plots show a right-skewed distribution starting at x=0.
+
+Figure 11-6 shows three density functions of log-normal distributions. Example 1: log(x) is standard normal (mu=0, sigma=1), showing a peak at x=1. Example 2: log(x) is normal with m=2, s=0.5, showing a peak at x=e^2. Example 3: log(x) is normal with m=2, s=1.5, showing a peak at x=e^2. All plots show the probability density function f(x) on the y-axis against x on the x-axis.
+
+Figure 11-6 – Density functions of log-normal distributions
+
+### 11.2.6.1.2.1 Use-case: transformations
+
+A given data set can be checked whether it is distributed according to a log-normal distribution by computing the log of the data values and using one of the graphical tools mentioned before for verifying the normal distribution for the logged data. Empirical mean and standard deviation of the transformed data can then be used for estimating the parameters of the distribution, respectively.
+
+Similarly, other transformation-based distributions can be derived from the normal distribution, for instance for the square-root transformation $\sqrt{x} \sim IN(\mu, \sigma^2)$ or the reciprocal transformation $1/x \sim IN(\mu, \sigma^2)$ . A general concept based on power-transformations of $x$ was proposed in [b-Box].
+
+### 11.2.6.1.3 Exponential distribution
+
+For modelling arrival processes, often the negative exponential distribution is used. The relevant parameter for this distribution is $\lambda$ which symbolizes the life cycle of a process. Concerning arrival processes, $\lambda$ is named the inter-arrival rate of succeeding events.
+
+| Exponential distribution | |
+|--------------------------|--------------------------------------------------------------------------------------------------|
+| Notation | $X \sim \text{Exp}(\lambda)$ |
+| Parameters | $\lambda > 0$ |
+| PDF | $f(x) = \lambda \exp(-\lambda x)$ if $x \geq 0$ |
+| CDF | $F(x) = 1 - \exp(-\lambda x)$ if $x \geq 0$ |
+| Expected value | $E\{X\} = \frac{1}{\lambda}$ |
+| Variance | $Var\{X\} = \frac{1}{\lambda^2}$ |
+| Remarks | Life-cycle description, survival function:
Survival probability $P(X > x) = \exp(-\lambda x)$ |
+
+
+
+The figure displays three separate plots, each representing the probability density function (PDF) of an exponential distribution with a different rate parameter $\lambda$ . All three plots share the same x-axis, labeled 'x', ranging from 0 to 7. The first plot, titled 'Negative Exponential Example Lambda is 0.5', has a y-axis from 0.0 to 0.6, with the curve starting at (0, 0.5). The second plot, titled 'Negative Exponential Example Lambda is 1', has a y-axis from 0.0 to 1.0, with the curve starting at (0, 1.0). The third plot, titled 'Negative Exponential Example Lambda is 2', has a y-axis from 0.0 to 2.0, with the curve starting at (0, 2.0). All curves show a rapid decay as x increases, with the curve for $\lambda=2$ decaying the fastest and the curve for $\lambda=0.5$ decaying the slowest.
+
+Three graphs showing the density functions of negative exponential distributions for different values of Lambda (0.5, 1, and 2). Each graph shows a red curve starting at a point on the y-axis and decaying towards the x-axis. The x-axis is labeled 'x' and ranges from 0 to 7. The y-axis ranges from 0.0 to 0.6 for Lambda=0.5, 0.0 to 1.0 for Lambda=1, and 0.0 to 2.0 for Lambda=2.
+
+Figure 11-7 – Density functions of negative exponential distributions
+
+### 11.2.6.1.4 Weibull distribution
+
+The Weibull distribution is a heavy-tailed distribution which means the distribution is skewed with a non-negligible part of the probability mass in the tail. This distribution can be used to describe processes which have a rare frequency, but which are not negligible due to their weight.
+
+| Weibull distribution | |
+|----------------------|----------------------------------------|
+| Notation | $X \sim \text{Weibull}(\alpha, \beta)$ |
+
+| | Weibull distribution |
+|----------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| Parameters | $\alpha$ with $\alpha \geq 0$ ,
$\beta$ with $\beta > 0$ |
+| PDF | $f_x(x) = \alpha\beta x^{\beta-1} \exp(-\alpha x^\beta)$ if $x > 0$ |
+| CDF | $F_x(x) = 1 - \exp(-\alpha x^\beta)$ if $x \geq 0$ |
+| Expected value | $E\{X\} = \alpha^{-\frac{1}{\beta}} \Gamma\left(\frac{1}{\beta} + 1\right)$
with $\Gamma$ Gamma function |
+| Variance | $Var\{X\} = \alpha^{-\frac{2}{\beta}} \left[ \Gamma\left(\frac{2}{\beta} + 1\right) - \left( \Gamma\left(\frac{1}{\beta} + 1\right) \right)^2 \right]$ with $\Gamma$ Gamma function |
+| Remarks | Fatigue of material
Weibull (2, $\beta$ ) is a Rayleigh distribution with parameter $\beta$ . Rayleigh is used for description of fading effects. |
+
+The Gamma function is defined as the integral function $\Gamma(x) = \int_0^\infty \exp(-t) t^{x-1} dt$ . One important relationship for the Gamma function is given by $\Gamma(x+1) = x \cdot \Gamma(x)$ . For integer values $n$ this relation transforms into $\Gamma(n) = (n-1)!$
+
+
+
+**Weibull Example 1**
+Weibull data with $\alpha = 1$ and $\beta = 1$
+
+**Weibull Example 2**
+Weibull data with $\alpha = 2$ and $\beta = 1$
+
+**Weibull Example 3**
+Weibull data with $\alpha = 3$ and $\beta = 1$
+
+Three plots showing Weibull density functions for different parameters. Plot 1 (alpha=1, beta=1) shows a monotonically decreasing curve starting at (0,1). Plot 2 (alpha=2, beta=1) shows a unimodal curve peaking at x=0.7. Plot 3 (alpha=3, beta=1) shows a unimodal curve peaking at x=0.4. All plots have x-axis from 0 to 7.
+
+**Figure 11-8 – Density functions of Weibull distributions**
+
+**11.2.6.1.5 Pareto distribution**
+
+The Pareto function also models a heavy tailed distribution. One common use-case of this distribution is the modelling of packet-oriented data traffic. For example, the size of HTTP requests and replies as well as FTP downloads can be described as a Pareto function.
+
+Figure 11-9 shows density functions of Pareto distributions.
+
+| Pareto distribution | |
+|---------------------|------------------------------------------------------------------------------------|
+| Notation | $X \sim \text{Pareto}(c, \alpha)$ |
+| Parameters | $c$ scale and location parameter
$\alpha$ shape parameter |
+| PDF | $f(x) = \alpha \cdot x^{-(\alpha+1)} \cdot c^\alpha$ for $x > c$ |
+| CDF | $F(x) = 1 - \left(\frac{c}{x}\right)^\alpha$ |
+| Expected value | $E\{X\} = \frac{c}{\alpha-1}$ for $\alpha > 1$ |
+| Variance | $\text{Var}\{X\} = \frac{c\alpha}{(\alpha-1)^2 \cdot (\alpha-2)}$ for $\alpha > 2$ |
+| Remarks | |
+
+
+
+Three plots showing the density functions of Pareto distributions with different parameters. Pareto Example 1: alpha = 1, c = 1. Pareto Example 2: alpha = 2, c = 1. Pareto Example 3: alpha = 4, c = 1. All plots show a red curve starting at x=1 and decaying towards zero as x increases. The curves are labeled 'Pareto Example 1', 'Pareto Example 2', and 'Pareto Example 3' respectively.
+
+Figure 11-9 – Density functions of Pareto distributions
+
+### 11.2.6.1.6 Extreme distribution (Fisher-Tippet distribution)
+
+For modelling extremely seldom events with a high and negligible influence, the extreme distribution may be appropriate.
+
+Example 1: In service probing, this distribution for example relates to the amount of data which is transferred via FTP data connections. Whereas most of the users generate traffic in the range of some ten or hundred megabytes, occasionally there may be users which like to transfer, for example, 10 gigabytes in one session. When modelling the overall FTP data traffic, these users cannot be neglected due to their immense data volume, but their occurrence probability is very low.
+
+Example 2: Concerning insurance cases, single incidents which require a very high financial effort arise when for example an explosion eliminates a complete factory building. Again, due to the high financial impact, these cases have to be taken into account even though that they occur rarely.
+
+Figure 11-10 shows density functions of extreme distributions.
+
+| | Extreme distribution |
+|----------------|------------------------------------------------------------------------------------------------------------------------------------------|
+| Notation | $X \sim \text{Extreme}(\alpha, \beta)$ |
+| Parameters | $\alpha$ shape parameter
$\beta$ scale parameter |
+| PDF | $f(x) = \frac{1}{\beta} \cdot \exp\left(-\frac{x-\alpha}{\beta}\right) \cdot \exp\left[-\exp\left(-\frac{x-\alpha}{\beta}\right)\right]$ |
+| CDF | $F(x) = \exp\left[-\exp\left(-\frac{x-\alpha}{\beta}\right)\right]$ |
+| Expected value | $E\{x\} = \alpha + \beta\gamma$
with $\gamma \approx 0,57721566$ constant of
Euler-Mascheroni |
+| Variance | $\text{Var}\{x\} = \frac{\Pi^2 \beta^2}{6} \text{ for } \alpha > 2$ |
+| Remarks | |
+
+
+
+Three density function plots for the Extreme distribution. Example 1 (alpha=1, beta=1) shows a peak at x=1 with a height of ~0.37. Example 2 (alpha=4, beta=1) shows a peak at x=4 with a height of ~0.37. Example 3 (alpha=4, beta=2) shows a peak at x=4 with a height of ~0.18. All plots have x-axis from 0 to 10 and y-axis from 0.0 to 0.4.
+
+**Figure 11-10 – Density functions of extreme distributions**
+
+### 11.2.6.2 Testing distributions
+
+Statistical tests are commonly applied to reject an assumption in favour of an alternative assumption. Therefore, most tests are based on some kind of measure of deviation. This may be the deviation of data from a model assumption or from an assumed mean value, a target value and so on. For computational ease, single deviations are often assumed to be normally distributed.
+
+Based on these concepts, three important testing distributions are introduced in the following, namely the Chi-square-, F- and Student t-distributions.
+
+#### 11.2.6.2.1 Chi-square distribution with $n$ degrees of freedom
+
+If the result of a service probing is assumed to be the result of a number of independent standard normal processes, this distribution provides a basis for testing against this assumption. For evaluation purposes concerning the $\chi^2$ distribution, see clause 11.2.6.4.
+
+A $\chi^2$ distribution represents a combination of $n$ independent random variables $Z_1, \dots, Z_n$ where each random variable is standard normal, i.e., $Z_i \sim N(0,1)$ . The combination is done according to:
+
+$$\sum_{i=1}^n Z_i^2 \sim \chi_n^2$$
+
+The result of this combination is called a "(central) $\chi^2$ distribution with $n$ degrees of freedom".
+
+Figure 11-11 shows density functions of Chi-square distributions.
+
+| | (Central) Chi-square distribution |
+|----------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| Notation | $X \sim \chi_n^2$
Random variable $X = \sum_{i=1}^n Z_i^2$ |
+| Parameters | $n$ : degrees of freedom
$Z_1, \dots, Z_n$ : independent standard normal random variables: $Z_i \sim N(0,1)$ |
+| PDF | $f(x) = \frac{1}{2^{\frac{n}{2}} \cdot \Gamma\left(\frac{n}{2}\right)} \cdot x^{\frac{n}{2}-1} \cdot \exp\left(-\frac{x}{2}\right) \text{ for } x > 0$ |
+| CDF | $F(x) = \int_{-\infty}^x f(\xi) d\xi$ No closed solution available |
+| Expected value | $E\{X\} = n$ |
+| Variance | $Var\{X\} = 2n$ |
+| Remarks | Combination of $n$ statistically independent $N(0,1)$ random variables (standard normal)
Approximation:
$F(x) = P(X \leq x) \cong \Phi\left(\frac{x - n}{\sqrt{2n}}\right)$ |
+
+
+
+Three plots showing the density functions of Chi-square distributions for n=1, n=3, and n=5. The x-axis represents x and the y-axis represents the density f(x). The plot for n=1 shows a curve starting at infinity for x=0 and decaying towards zero. The plot for n=3 shows a curve starting at zero for x=0, peaking at x=1, and then decaying. The plot for n=5 shows a curve starting at zero for x=0, peaking at x=3, and then decaying.
+
+Figure 11-11 – Density functions of Chi-square distributions
+
+#### 11.2.6.2.1.1 Further relations
+
+The referenced gamma function is defined as the integral function:
+
+$$\Gamma(x) = \int_0^{\infty} \exp(-t) \cdot t^{x-1} dt$$
+
+Additional useful relations according to this function are:
+
+$$\Gamma(x+1) = x \cdot \Gamma(x)$$
+
+and
+
+$$\Gamma(n) = (n-1)! \text{ if } x = n \text{ is an integer value}$$
+
+#### 11.2.6.2.1.2 Relation to empirical variance
+
+- If the mean value $\mu$ is known, the empirical variance of $n$ normally distributed random variables reads $s_{\mu}^2 = \frac{1}{n} \cdot \sum_{i=1}^n (x_i - \mu)^2$ . With this piece of information, a chi-square distribution is given for the following expression: $n \cdot \frac{s_{\mu}^2}{\sigma^2} \sim \chi_n^2$ .
+- Without knowledge of $\mu$ , the empirical variance $s^2 = \frac{1}{n-1} \cdot \sum_{i=1}^n (x_i - \bar{x})^2$ estimates the variance of the process. The appropriate relation in this case reads $(n-1) \cdot \frac{s^2}{\sigma^2} \sim \chi_{n-1}^2$ .
+
+#### 11.2.6.2.2 Student t-distribution
+
+If a standard normal and a statistically independent chi-square distribution with $n$ degrees of freedom are combined, according to $X = \frac{U}{\sqrt{Z/n}}$ , where $Z \sim \chi^2$ (chi-square distributed) and $U \sim N(0,1)$ (standard normal distributed), the constructed random variable $X$ is said to be $t$ -distributed with $n$ degrees of freedom. Alternatively, the denomination "Student t-distribution" can be used.
+
+Figure 11-12 shows density functions of student t-distributions.
+
+| | Student t-distribution |
+|----------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| Notation | $X \sim t_n$
Random variable $X = \frac{U}{\sqrt{Z/n}}$ with $U \sim N(0,1)$ , $Z \sim \chi_n^2$ , independent. |
+| Parameters | $n$ : degrees of freedom |
+| PDF | $f(x) = \frac{\Gamma\left(\frac{n+1}{2}\right)}{\Gamma\left(\frac{n}{2}\right) \cdot \sqrt{\pi \cdot n}} \cdot \left(1 + \frac{x^2}{n}\right)^{-\frac{n+1}{2}}$ |
+| CDF | $F(x) = \int_{-\infty}^x f(\xi) d\xi$ No closed solution available |
+| Expected value | $n \geq 2 : E\{Z\} = 0$ |
+
+| | |
+|----------|-------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| Variance | $n \geq 3 : Var\{Z\} = \frac{n}{n-2}$ |
+| Remarks | The PDF is a symmetric function with symmetry axis $x = 0$ .
Additional relation for $\alpha$ -quantiles $t_{n;\alpha} : t_{n;\alpha} = -t_{n;1-\alpha}$ |
+
+
+
+Three plots showing the density functions of student t-distributions for n=2, n=4, and n=40. Each plot shows a symmetric bell-shaped curve centered at x=0. The curves become narrower and taller as n increases. The x-axis ranges from -3 to 3, and the y-axis ranges from 0.0 to 0.4.
+
+**Figure 11-12 – Density functions of student t-distributions**
+
+#### 11.2.6.2.2.1 Relation to normal distribution
+
+It may not be obvious, but $t$ -distributions with large number of degrees of freedom may be approximated by a standard normal distribution.
+
+- The standardization of normal variables was covered before: If $X \sim N(\mu, \sigma^2)$ , then $(X-\mu)/\sigma \sim N(0, 1)$ .
+- Consider the case of data assumed to be normal with unknown variance. As stated before, the empirical variance is then related to a chi-square distribution. The empirical mean and variance of $n$ normally distributed ( $N(\mu, \sigma^2)$ ) random variables $X_1, X_2, \dots, X_n$ are given by:
+
+$$\bar{X} = \frac{1}{n} \cdot \sum_{i=1}^n X_i$$
+
+$$S^2 = \frac{1}{n-1} \cdot \sum_{i=1}^n (X_i - \bar{X})^2$$
+
+With these relations, the relation between the $t$ -distribution and the $n$ normal distributed random variables is given by:
+
+$$\sqrt{n} \cdot \frac{\bar{X} - \mu}{\sqrt{S^2}} \sim t_{n-1}.$$
+
+#### 11.2.6.2.3 F distribution
+
+The $F$ distribution is a combination of $m$ standard normal distributed random variables $Y_i$ and $n$ standard normal distributed random variables $V_i$ which are combined as described below. Again, $m$ and $n$ are called "degrees of freedom" of this distribution.
+
+This distribution is often used for computation and evaluation purposes, for example in relation with confidence intervals for the binomial distribution (Pearson-Clopper formula). In general, it compares two types of deviations, for instance if two different models are fitted.
+
+Figure 11-13 shows density functions of F distributions.
+
+| | F distribution |
+|----------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| Notation | $X \sim F_{m,n}$
Random variable $X = \frac{\frac{1}{m} \cdot \sum_{i=1}^m Y_i^2}{\frac{1}{n} \cdot \sum_{i=1}^n V_i^2}$ |
+| Parameters | $m, n$ : degrees of freedom
$Y_1, \dots, Y_m$ : independent random variables according to $N(0,1)$
$V_1, \dots, V_n$ : independent random variables according to $N(0,1)$ |
+| PDF | $f(x) = \frac{\left(\frac{m}{n}\right)^{\frac{m}{2}} \cdot x^{\frac{m}{2}-1}}{B\left(\frac{m}{2}, \frac{n}{2}\right)} \cdot \left(1 + \frac{m}{n} \cdot x\right)^{-\frac{m+n}{2}} \text{ for } x > 0$ with
Eularian beta function
|
+| CDF | $F(x) = \int_{-\infty}^x f(\xi) d\xi$ No closed solution available
|
+| Expected value | $n > 2 : E\{Z\} = \frac{n}{n-2}$ |
+| Variance | $n > 4 : Var\{Z\} = \frac{2n^2(m+n-2)}{m(n-2)^2(n-4)}$ |
+| Remarks | A $F_{m,n}$ related distribution can be interpreted as the quotient of a $\chi_m^2$ distribution and a $\chi_n^2$ distribution multiplied with $\frac{n}{m}$ . |
+
+
+
+**F Example 1**
+F data with m = 4 and n = 40
+
+Density function plot for F distribution with m=4 and n=40. The curve starts at (0,0), peaks at approximately x=0.5 with a height of 0.7, and then decays towards the x-axis. The x-axis ranges from 0 to 7, and the y-axis ranges from 0.0 to 1.0.
+
+
+
+**F Example 2**
+F data with m = 2 and n = 2
+
+Density function plot for F distribution with m=2 and n=2. The curve starts at (0,1) and decays monotonically towards the x-axis. The x-axis ranges from 0 to 7, and the y-axis ranges from 0.0 to 1.0.
+
+
+
+**F Example 3**
+F data with m = 40 and n = 4
+
+Density function plot for F distribution with m=40 and n=4. The curve starts at (0,0), peaks at approximately x=0.5 with a height of 0.6, and then decays towards the x-axis. The x-axis ranges from 0 to 7, and the y-axis ranges from 0.0 to 1.0.
+
+**Figure 11-13 – Density functions of F distributions**
+
+### 11.2.6.2.3.1 Quantiles
+
+For quantile computation purposes, the following relations may be useful:
+
+$$F_{n_1, n_2; 1-\gamma} = \frac{1}{F_{n_2, n_1; \gamma}}$$
+
+In general, quantile values of this distribution are tabulated.
+
+### 11.2.6.2.3.2 Approximation of quantiles
+
+If the desired quantile value cannot be found in tables, the following approximation may be helpful:
+
+If the $\gamma$ -quantile is wanted with $\gamma$ in the range of $0.5 < \gamma < 1$ , the relation
+
+$$F_{n_1, n_2; \gamma} \cong \exp(u \cdot a - b)$$
+
+applies where $u = u_\gamma$ is the $\gamma$ -quantile of the standard normal distribution $N(0,1)$ .
+
+The symbols $a$ and $b$ are derived from the following equations:
+
+$$\begin{aligned} a &= \sqrt{2d + cd^2} \\ b &= 2 \cdot \left( \frac{1}{n_1 - 1} - \frac{1}{n_2 - 1} \right) \cdot \left( c + \frac{5}{6} - \frac{d}{3} \right) \\ c &= \frac{(u_\gamma)^2 - 3}{6} \\ d &= \frac{1}{n_1 - 1} + \frac{1}{n_2 - 1} \end{aligned}$$
+
+### 11.2.6.2.3.3 Relations to other distributions
+
+When the F distribution comes to usage, the following relations may ease the handling of this distribution:
+
+- Relation to $t$ distribution for $n_1 = 1$ : $F_{1, n_2; \gamma} = \left( t_{n_2; \frac{1+\gamma}{2}} \right)^2$ .
+- Relation to $\chi^2$ distribution for $n_2 \rightarrow \infty$ : $F_{n_1, \infty; \gamma} = \frac{1}{n_1} \cdot \chi_{n_1; \gamma}^2$ .
+- If $n_1 \rightarrow \infty$ and $n_2 \rightarrow \infty$ , the distribution simplifies to: $F_{\infty, \infty; \gamma} = 1$
+
+## 11.2.6.3 Discrete distributions
+
+Discrete distributions describe situations where the outcome of measurements is restricted to integer values. For example, the results of service access tests show either that service access is possible (mostly represented by a logical "1" value) or that it is not possible (mostly represented by a logical "0" value). Depending on the circumstances under which such "drawing a ball out of a box" tests are executed, different statistical distributions apply as shown in clauses 11.2.6.3.1 to 11.2.6.3.4.
+
+### 11.2.6.3.1 Bernoulli distribution
+
+The starting point of different discrete distributions is given by the Bernoulli distribution. It simply describes the probability $p$ of a positive outcome of a single test where only two states are allowed, generally a positive one and a negative one. As soon as more than one single test is executed, further discrete distribution may be applied as shown in the following clauses.
+
+Figure 11-14 shows Density functions for Bernoulli distributions.
+
+| Bernoulli distribution | |
+|-------------------------------|---------------------------------------------------------------------------------------------------------------------|
+| Notation | $X \sim \text{Bernoulli}(p)$ |
+| Parameters | $p \in (0,1)$ |
+| PDF | $p(x) = \begin{cases} 1-p & \text{if } x=0 \\ p & \text{if } x=1 \\ 0 & \text{otherwise} \end{cases}$ |
+| CDF | $F(x) = \begin{cases} 0 & \text{if } x < 0 \\ 1-p & \text{if } 0 \leq x < 1 \\ 1 & \text{if } 1 \leq x \end{cases}$ |
+| Expected value | $E\{X\} = p$ |
+| Variance | $\text{Var}\{X\} = p \cdot (1-p)$ |
+| Remarks | |
+
+
+
+Three plots showing the probability density functions (PDFs) for Bernoulli distributions with p=0.8, p=0.6, and p=0.3. Each plot shows two vertical red lines at x=0 and x=1. For p=0.8, the line at x=1 is taller (height 0.8) and the line at x=0 is shorter (height 0.2). For p=0.6, the line at x=1 is taller (height 0.6) and the line at x=0 is shorter (height 0.4). For p=0.3, the line at x=0 is taller (height 0.7) and the line at x=1 is shorter (height 0.3). The x-axis ranges from -1.0 to 2.0, and the y-axis (f(x)) ranges from 0.0 to 1.0.
+
+**Figure 11-14 – Density functions for Bernoulli distributions**
+
+### 11.2.6.3.2 Binomial distribution
+
+Whenever the outcome of a test is either true or false, the binomial distribution can be applied. Where a "black or white" interpretation of results is appropriate, this distribution is able to describe the measurement process. Due to this "yes or no" character, the binomial distribution can be interpreted as the result of different Bernoulli tries. Relevant examples related to service probing are service access issues (e.g., call success rate, SMS send failure ratio, etc.). For a high number of measurement results, the distribution can be replaced by the normal distribution as a first approximation as shown in clause 11.2.6.1.1.
+
+**Precondition:** To determine the CDF of a binomial distribution with relation to different tests, the single events have to be independent from each other. This means that the probability of a successful outcome of different consecutive tests **must not change**. In consequence, this means a memory-less process where the result of a succeeding test is not related to the outcome of its predecessor(s).
+
+Figure 11-15 shows density functions of binomial distributions.
+
+| | Binomial distribution |
+|----------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| Notation | $X \sim \text{Bin}(n, p)$ |
+| Parameters | $n$ Number of tests
$m$ Number of successful test outcomes
$p = \frac{m}{n}$ Observed probability of successful outcomes
$q = 1 - p$ Observed probability of unsuccessful outcomes |
+| PDF | $P(X = k) = \binom{n}{k} p^k (1 - p)^{n-k} = b(n, p, k)$ with $k = 0, 1, 2, \dots, n$ |
+| CDF | $P(X \leq k_0) = \sum_{k=0}^{k_0} \binom{n}{k} p^k (1 - p)^{n-k}$ with $k = 0, 1, 2, \dots, n$ and $k_0 \geq k$ |
+| Expected value | $E\{X\} = n \cdot p$ |
+| Variance | $\text{Var}\{X\} = n \cdot p \cdot q = n \cdot p \cdot (1 - p)$ |
+| Remarks | Related to $F$ distribution |
+
+
+
+Three bar charts showing the probability density functions (PDFs) of binomial distributions. Binomial 1: n=10, p=0.25, skewed right, peak at x=2. Binomial 2: n=10, p=0.5, symmetric, peak at x=5. Binomial 3: n=20, p=0.5, symmetric, peak at x=10. All charts have f(x) on the y-axis (0.00 to 0.35) and x on the x-axis.
+
+**Figure 11-15 – Density functions of binomial distributions**
+
+For computation purposes, the following relation between the binomial distribution and the $F$ distribution may be useful:
+
+$$P(X < x) = 1 - P\left(F \leq \frac{n-x}{x+1} \cdot \frac{p}{1-p}\right)$$
+
+In this formula, $F$ represents a $F$ distributed random variable with $2 \times (x+1)$ , $2 \times (n-x)$ , degrees of freedom.
+
+### 11.2.6.3.3 Geometric distribution
+
+The geometric distribution typically describes the following situation: A number of Bernoulli trials are executed consecutively. Each of these trials has a success probability $p$ . By use of the geometrical distribution, one can determine the probability of a successful outcome of a Bernoulli trial after $x$ unsuccessful outcomes.
+
+Scenarios where this computation may be of interest are for example the occurrence of the first success after $x$ failures, or related to service probing, the number of failed service access attempts before the first successful attempt.
+
+Figure 11-16 shows density functions of geometric distributions.
+
+| Geometric distribution | |
+|------------------------|------------------------------------------------------------------------------------------------------------------|
+| Notation | $X \sim G(p)$ |
+| Parameters | $p \in (0,1)$ |
+| PDF | $p(x) = \begin{cases} p(1-p)^x & \text{if } x \in \{0, 1, \dots\} \\ 0 & \text{otherwise} \end{cases}$ |
+| CDF | $F(x) = \begin{cases} 1-(1-p)^{\lfloor x \rfloor + 1} & \text{if } x \geq 0 \\ 0 & \text{otherwise} \end{cases}$ |
+| Expected value | $E\{X\} = \frac{1-p}{p}$ |
+| Variance | $Var\{X\} = \frac{1-p}{p^2}$ |
+| Remarks | |
+
+
+
+The figure displays three separate bar charts, each representing the PDF of a geometric distribution with a different success probability $p$ .
+
+- Geometric 1 (p is 0.25):** The distribution is relatively spread out. The highest probability is at $x=0$ (approx. 0.25), followed by $x=1$ (approx. 0.19), $x=2$ (approx. 0.14), and so on.
+- Geometric 2 (p is 0.5):** The distribution is more concentrated. The probability at $x=0$ is 0.5, $x=1$ is 0.25, $x=2$ is 0.125, etc.
+- Geometric 3 (p is 0.8):** The distribution is highly concentrated at $x=0$ . The probability at $x=0$ is 0.8, $x=1$ is 0.16, $x=2$ is 0.032, etc.
+
+Three bar charts showing the probability density functions (PDFs) of geometric distributions for p = 0.25, 0.5, and 0.8. The x-axis represents the number of failures (x) from 0 to 10, and the y-axis represents the probability p(x) from 0.0 to 0.8. As p increases, the distribution becomes more concentrated at x=0.
+
+Figure 11-16 – Density functions of geometric distributions
+
+#### 11.2.6.3.4 Poisson distribution
+
+The Poisson distribution is also called "distribution of rare events". Generally, this distribution relates to the number of events within a certain time of period under the precondition that the events occur at a constant rate $\lambda$ . The Poisson distribution often is used to describe call arrivals in a transmission system, especially the current number of processed service attempts in a system.
+
+Figure 11-17 shows density functions of Poisson distributions.
+
+| | Poisson distribution |
+|----------------|-----------------------------------------------------------------------------------------------|
+| Notation | $X \sim Po(\lambda)$ |
+| Parameters | $\lambda$ |
+| PDF | $P(X = k) = \frac{\lambda^k}{k!} \exp(-\lambda)$ with $k = 0, 1, 2, \dots, n$ |
+| CDF | $P(X \leq k) = \sum_{i=0}^k \frac{\lambda^i}{i!} \exp(-\lambda)$ with $k = 0, 1, 2, \dots, n$ |
+| Expected value | $E\{X\} = \lambda$ |
+| Variance | $Var\{X\} = \lambda$ |
+| Remarks | Related to $\chi^2$ distribution |
+
+
+
+The figure displays three separate bar charts, each representing the probability mass function (PMF) of a Poisson distribution with a different value of the parameter $\lambda$ .
+
+- Poisson 1:** $\lambda = 0.5$ . The distribution is highly skewed to the right, with the highest probability at $x = 0$ (approximately 0.61).
+- Poisson 2:** $\lambda = 1$ . The distribution is more spread out, with the highest probabilities at $x = 0$ and $x = 1$ (both approximately 0.37).
+- Poisson 3:** $\lambda = 3$ . The distribution is the most spread out of the three, with the highest probabilities around $x = 2$ and $x = 3$ (approximately 0.22 and 0.22 respectively).
+
+Three bar charts showing the probability density functions (PDFs) of Poisson distributions for Lambda = 0.5, 1, and 3. Each chart plots f(x) against x for x values 0, 1, 2, 3, 4, 5, 6, 7, 8. As Lambda increases, the distribution shifts to the right and becomes broader.
+
+**Figure 11-17 – Density functions of Poisson distributions**
+
+For computation purposes, the following relation between the Poisson distribution and the $\chi^2$ distribution may be useful:
+
+$$P(X \leq x) = 1 - P(\chi^2 \leq 2\lambda)$$
+
+In this formula, $\chi^2$ represents a $\chi_{2x+2}^2$ distributed random variable.
+
+#### 11.2.6.4 Transitions between distributions and appropriate approximations
+
+Depending on the number of available measurement results, different distributions can be applied to handle the results. In this clause, some useful transitions between common distributions and their required conditions are discussed.
+
+##### 11.2.6.4.1 Approximation of binomial distribution by Poisson distribution
+
+The binomial distribution can be approximated by the Poisson distribution if:
+
+- the probability $p$ is small (rule of thumb: $p < 0.1$ ); and
+- the number of executed test cases $n$ is high enough (rule of thumb: $n > 30$ ).
+
+The approximation of a binomial distributed quantity by a Poisson distribution is given by:
+
+$$P(X = k) \cong \frac{\lambda^k}{k!} \cdot \exp(-\lambda)$$
+
+where the Poisson distribution parameter $\lambda$ is given by:
+
+$$\lambda = p \times n$$
+
+#### 11.2.6.4.2 Approximation of binomial distribution by normal distribution
+
+If a binomial distribution fulfils the rule of thumb:
+
+$$n \times p \times q \geq 9$$
+
+then it can be approximated by the Normal distribution:
+
+$$B(n, p) \cong N(n \cdot p, n \cdot p \cdot q)$$
+
+The approximation in detail reads:
+
+$$P(X \leq x) \cong \Phi\left(\frac{X - n \cdot p}{\sqrt{n \cdot p \cdot q}}\right)$$
+
+Especially for smaller numbers of $n$ the following approximation may be more favourable:
+
+$$P(x_1 \leq X \leq x_2) \cong \Phi\left(\frac{x_2 - n \cdot p + 0,5}{\sqrt{n \cdot p \cdot q}}\right) - \Phi\left(\frac{x_1 - n \cdot p - 0,5}{\sqrt{n \cdot p \cdot q}}\right)$$
+
+#### 11.2.6.4.3 Approximation of Poisson distribution by normal distribution
+
+According to the Poisson limit theorem, the Poisson distribution can be approximated by the normal distribution if the distribution parameter $\lambda$ fulfils the following relation:
+
+$$\lambda = p \times n \geq 9$$
+
+which is quite similar to the transition from binomial to normal distribution.
+
+Then, the approximation reads:
+
+$$P(X \leq k) \cong \Phi\left(\frac{k - \lambda}{\sqrt{\lambda}}\right)$$
+
+#### 11.2.6.5 Truncated distributions
+
+Due to resource constraints of measurement equipment, some measurements have to consider timeout values. By the use of timeouts, the maximum period of time in which measurement results are considered as relevant for the measurement is limited. The resulting density function then is clipped at the right-hand side. Truncation may also occur at both ends of a density function.
+
+For example, if the end-to-end delivery time of some message service is subject of a measurement, the introduction of timeout values may reduce the number of measurement samples. This is because all delivery times which are higher than the defined timeout value are discarded. By discarding some samples, the entirety of data is reduced which means that probabilities describing the measurement may be influenced.
+
+In general, truncation can be described by conditional probabilities. The condition is given by the timeout value. Furthermore, probabilities are then computed under the constraint of the timeout. Truncated normal and Poisson distributions are covered in more detail by [b-Mood].
+
+### **11.2.6.6 Distribution selection and parameter estimation**
+
+If a distribution is sought to describe a given data set, two steps have to be carried out. First, an appropriate distribution family (type of distribution) has to be selected and second, the corresponding parameters specifying this distribution have to be estimated. Test procedures or graphical methods may be applied for the first step, parameter estimation procedures are needed for the second.
+
+#### **11.2.6.6.1 Test procedures**
+
+The formulation of tests is covered in detail in clause 11.2.7.1. In this clause, three well-known tests that may be used for checking distributional assumptions are described briefly. It focuses mainly on the fundamental ideas leading to these tests.
+
+All test procedures are based on comparisons between assumed and empirical distribution. That is, from the data on hand, the underlying distribution is guessed and then verified by applying one of the test procedures described in clauses 11.2.6.6.1.1 to 11.2.6.6.1.3.
+
+##### **11.2.6.6.1.1 Chi-square test**
+
+The main idea of the Chi-square test is to test whether a set of data comes from a normal distribution by building classes and checking whether the expected number of observations and the number of data in each class are similar. If the deviations between both numbers – in terms of squared differences – exceeds a corresponding $\chi^2$ value, the distribution assumed has to be rejected.
+
+##### **11.2.6.6.1.2 Kolmogorov-Smirnov test**
+
+The Kolmogorov-Smirnov test is based on the cumulative distribution functions of the theoretical (assumed) and empirical distribution of the data at hand. The main idea is that the distributional assumption is rejected, if the maximum vertical distance between both cumulative density functions (CDFs) exceeds a critical value.
+
+##### **11.2.6.6.1.3 Shapiro-Wilk test**
+
+Shapiro and Wilk suggested a test procedure that is based on quantiles and related to the QQ-Plot introduced in clause 11.3.1.3. The main idea of this test is to compare the sum of squared deviations between the points of the QQ-Plot and the best fitting straight line with a given $\chi^2$ -value.
+
+#### **11.2.6.6.2 Parameter estimation methods**
+
+Most frequently applied methods for parameter estimation are maximum-likelihood or moment estimation methods. For the mean of a normal distribution, both methods yield identical results. For further details see [b-Mood].
+
+### **11.2.7 Evaluation of measurement data**
+
+Related to active service probing, certain issues can become much easier to handle if it is able to describe the gathered data in a very compact way. One possible way to reach this aim is to execute different tests and thereby to check some assumptions. These assumptions are stated before any testing is done. They are called "hypotheses".
+
+From a slightly more theoretical point of view, this topic can be expressed as follows:
+
+With every measurement sample, some information about the investigated process is retrieved. Since commonly the characteristics of the process are unknown, with every piece of additional information (i.e., every sample) the degree of knowledge increases. This knowledge is formalized by the application of statistical tests or the determination of confidence intervals for the distributional parameters of interest.
+
+The idea of statistical tests and some simple examples are presented in clause 11.2.7.1. The construction of confidence intervals and the relation between test and confidence intervals are covered in clause 11.2.7.2.
+
+### 11.2.7.1 Statistical tests
+
+Statistical tests are introduced by specifying the test components first and afterward distinguishing different test classes and giving examples where appropriate.
+
+#### 11.2.7.1.1 Formulation of statistical tests
+
+Statistical tests are formulated by specifying the following components:
+
+- **(Null-)Hypothesis:** This hypothesis is commonly denoted by $H$ or $H_0$ .
+
+Example 1: $H: \mu = 60$ .
+
+- **Alternative hypothesis:** This one is commonly denoted by $A$ or $H_1$ .
+
+Example 2: $A: \mu \neq 60$ or $A: \mu > 60$ .
+
+- **Test statistic:** A test statistic is derived so that it is sensitive for deviation from the hypothesis in favour of the alternative. That is, the meaning of the test statistic is to notice if in fact $A$ is true instead of $H$ . Test statistics are commonly denoted by $T$ .
+- **Testing rule and critical value:** The testing rule states the condition under which $H$ is rejected in favour of the alternative $A$ . So it represents something like a switching condition.
+
+Example 3: "Reject $H$ , if $\bar{x} > c$ " or "Reject $H$ , if $|\bar{x}| > c$ ".
+
+The value $c$ is called "critical value".
+
+- **Type I: Error level $\alpha$ :** The probability of rejection $H$ , although true, is controlled by the value $\alpha$ . The specification of $\alpha$ has direct impact on $c$ and thereby on the testing rule. Commonly, the type I error is restricted to 5 per cent (i.e., $\alpha = 0.05$ ) or 1 per cent (i.e., $\alpha = 0.01$ ).
+
+A statistical test is carried out by specifying all of the above components, computing the test statistic and comparing it with the critical value. Test results are usually documented by reporting the value of the test statistic as well as the corresponding test result. Alternatively, the so called $p$ -value may be reported. This value measures the "significance" or a test result in the way that if the $p$ -value is smaller than the error level $\alpha$ , the hypothesis is rejected, otherwise it may not be rejected. A $p$ -value corresponds to the smallest $\alpha$ -level for which the test would have been rejected.
+
+#### 11.2.7.1.2 Classes of statistical tests
+
+Commonly, statistical tests are formulated in the way that the alternative hypothesis makes the statement that one wishes to prove in a statistical sense. That is, in general, tests seek to reject a given hypothesis and are therefore formulated accordingly. This is done due to the fact that if the Type I error is specified, a rejection implies that the hypothesis is untrue with user-defined certainty.
+
+However, a number of test procedures exist that differ from the mentioned general test philosophy. Some of these examples are the tests used for selecting distributions (see clause 11.2.6.6). Their purpose is to support the hypothesis. Nevertheless, it is almost impossible to prove any equality hypothesis. Therefore, the test result can either be that there is no hint that the hypothesis is violated or that there is evidence that the assumed distribution is not appropriate. In the following, it is assumed that one wishes to reject the hypothesis in favour of the alternative hypothesis.
+
+Two major classes of tests are distinguished, namely one-sample and two-sample tests.
+
+- If a test is based on only one data set for which a reference alternative is to be checked, this is a one-sample test.
+- On the other hand, two data sets may be compared by testing for instance the hypothesis $H$ related to the multimedia messaging service (MMS).
+
+$H$ : MMS-E2E-delivery time [this week] > MMS-E2E-delivery time [last week] against the alternative hypothesis that the MMS-E2E-delivery time was reduced from last week to this week.
+
+Furthermore, tests that are based on distributional assumptions and distribution-free tests are distinguished. Most distribution-based tests are testing for the location and dispersion/variation of an assumed distribution. For two-sample tests, both samples are assumed to be from the same type of distribution, but possibly with different parameters, for instance different location. In contrast, distribution-free tests should be applied, if there is not enough knowledge about the distribution of the data. However, distribution-free tests are in general less powerful, therefore distribution-based tests should be preferred, if appropriate.
+
+### 11.2.7.1.3 Tests for normal and binomial data
+
+In the following clauses, two of the main use-cases of statistical data are taken into concern. These clauses deal with test for normal distributed and binomial distributed data.
+
+#### 11.2.7.1.3.1 One-sample tests for normal data
+
+If data are from a normal distribution with known variance $\sigma_0^2$ , i.e., $X_1, \dots, X_n \sim N(\mu, \sigma_0^2)$ , three different location tests may be carried out. All of these compare the location of a given sample to a value $\mu_0$ that may be chosen arbitrarily.
+
+- Test for $H: \mu = \mu_0$ vs. $A: \mu \neq \mu_0$ : The corresponding test statistic is given by $T = \left| \sqrt{n} \frac{\bar{x} - \mu}{\sigma_0} \right|$ . $H$ is rejected, if $T > u_{1-\alpha/2}$ .
+- Test for $H: \mu \leq \mu_0$ vs. $A: \mu > \mu_0$ : The corresponding test statistic is given by $T = \left| \sqrt{n} \frac{\bar{x} - \mu_0}{\sigma_0} \right|$ . $H$ is rejected, if $T > u_{1-\alpha}$ (for $\alpha < 0.5$ ).
+- Test for $H: \mu \geq \mu_0$ vs. $A: \mu < \mu_0$ : The corresponding test statistic is again given by $T = \left| \sqrt{n} \frac{\bar{x} - \mu_0}{\sigma_0} \right|$ . $H$ is rejected, if $T < u_\alpha = -u_{1-\alpha}$ (for $\alpha < 0.5$ ).
+
+If data are from a normal distribution, but the variance is unknown, i.e., $X_1, \dots, X_n \sim N(\mu, \sigma^2)$ , the variance has to be estimated from the data and the above "normal-tests" are replaced by student $t$ -tests. In this case, the variance estimator:
+
+$$s^2 = \frac{1}{n-1} \sum_{i=1}^n (x_i - \bar{x})^2$$
+
+is applied. Test statistics are replaced as follows: $T = \left| \sqrt{n} \frac{\bar{x} - \mu_0}{S} \right|$ or $T = \sqrt{n} \frac{\bar{x} - \mu_0}{\sigma_0}$ , respectively.
+
+Critical values are given by the quantiles of the $t$ -distribution: $t_{n-1, 1-\alpha/2}$ , $t_{n-1, 1-\alpha}$ or $t_{n-1, \alpha}$ , respectively.
+
+If instead the variance is unknown and subject of a test, i.e., $X_1, \dots, X_n \sim N(\mu, \sigma^2)$ with unknown $\mu$ and $\sigma^2$ , the following tests comparing the variance of a given sample to a value $\sigma_0$ , that may be carried out.
+
+- Test for $H: \sigma = \sigma_0$ vs. $A: \sigma \neq \sigma_0$ : The corresponding test statistic is given by $T = (n-1)s^2/\sigma_0^2$ with $s^2 = \frac{1}{n-1} \sum_{i=1}^n (x_i - \bar{x})^2$ . $H$ is rejected, if $T > \chi^2_{1-\alpha/2, n-1}$ or $T < \chi^2_{\alpha/2, n-1}$ .
+- Test for $H: \sigma \leq \sigma_0$ vs. $A: \sigma > \sigma_0$ : The corresponding test statistic is again given by $T = (n-1)s^2/\sigma_0^2$ with $s^2$ as given above. $H$ is rejected, if $T > \chi^2_{1-\alpha, n-1}$ .
+- Test for $H: \sigma \geq \sigma_0$ vs. $A: \sigma < \sigma_0$ : Test statistic and empirical variance are as before. In this case, $H$ is rejected, if $T < \chi^2_{\alpha, n-1}$ .
+
+### 11.2.7.1.3.2 Two-sample tests for normal data
+
+In the case of two samples, that are to be compared, two very different situations are distinguished. The two samples can either be collected on the same observational units or can be observed independently. If both samples are from the same units, for example measuring the cut-off call ratio at different network elements before and after a new piece of software is installed, the two samples are called paired and two observations from the same unit will generally be correlated. In this case, the differences between both measurements for each unit are computed and the new observations $D_i = X_i - Y_i$ are assumed to be normal with expectation $\mu_D = \mu_X - \mu_Y$ . Then, the above tests for normal data may be applied, for instance to test for $\mu_D = 0$ , i.e., $\mu_X = \mu_Y$ to prove that there is a significant difference between both samples.
+
+For independent data from two samples, both assumed to be normally distributed with the same known variance, but possibly different expectations, i.e., $X_1, \dots, X_n \sim N(\mu_X, \sigma^2)$ and $Y_1, \dots, Y_m \sim N(\mu_Y, \sigma^2)$ , tests to compare both means are given as follows.
+
+The following test statistic $T$ is defined for testing the hypotheses:
+
+$$T = \frac{\bar{x} - \bar{y}}{\sigma \sqrt{\frac{1}{n} + \frac{1}{m}}}$$
+
+- Test for $H: \mu_X = \mu_Y$ vs. $A: \mu_X \neq \mu_Y$ : $H$ is rejected, if $|T| > u_{1-\alpha/2}$
+- Test for $H: \mu_X \leq \mu_Y$ vs. $A: \mu_X > \mu_Y$ : $H$ is rejected, if $T > u_{1-\alpha}$
+- Test for $H: \mu_X \geq \mu_Y$ vs. $A: \mu_X < \mu_Y$ : $H$ is rejected, if $T < u_\alpha$
+
+If the variance is unknown but assumed to be equal for both samples, the normal distribution is again replaced by a student t-distribution resulting in the following test procedures.
+
+$$T = \frac{\bar{x} - \bar{y}}{s \sqrt{\frac{1}{n} + \frac{1}{m}}} \text{ with } s = \frac{\sum_{i=1}^n x_i^2 - \frac{1}{n} \left( \sum_{i=1}^n x_i \right)^2 + \sum_{i=1}^m y_i^2 - \frac{1}{m} \left( \sum_{i=1}^m y_i \right)^2}{n + m - 2}$$
+
+- Test for $H: \mu_X = \mu_Y$ vs. $A: \mu_X \neq \mu_Y$ : $H$ is rejected, if $|T| > t_{1-\alpha/2, n+m-2}$ .
+- Test for $H: \mu_X \leq \mu_Y$ vs. $A: \mu_X > \mu_Y$ : $H$ is rejected, if $T > t_{1-\alpha, n+m-2}$ .
+- Test for $H: \mu_X \geq \mu_Y$ vs. $A: \mu_X < \mu_Y$ : $H$ is rejected, if $T < t_{\alpha, n+m-2}$ .
+
+In general, before carrying out one of the above tests, the assumption of equal variances has to be verified. This can be done by using the following test:
+
+- Test for $H: \sigma_X^2 = \sigma_Y^2$ vs. $A: \sigma_X^2 \neq \sigma_Y^2$ : The corresponding test statistic is given by $T = \frac{s_X^2}{s_Y^2}$ ,
+
+where:
+
+$$S_X^2 = \frac{1}{n-1} \sum_{i=1}^n (x_i - \bar{x})^2 \text{ and } S_Y^2 = \frac{1}{m-1} \sum_{i=1}^m (y_i - \bar{y})^2$$
+
+### 11.2.7.1.3.3 Test for binomial data
+
+For binomial data, tests for the probability of success $p$ may be carried out that compare the sample probability to some specified value $p_0$ . Three one-sample tests may be derived by computing the critical values under the three hypotheses.
+
+If $m$ is the number of successful trials, the first hypothesis is rejected, if:
+
+$$m > c_{1-\alpha/2} \text{ or } m < d_{\alpha/2}$$
+
+where:
+
+$$c_{1-\alpha/2} = \min_{k \in \{1, \dots, n\}} \sum_{i=k+1}^n \binom{n}{i} p_0^i (1-p_0)^{n-i} \leq \alpha$$
+
+and
+
+$$d_{\alpha/2} = \max_{k \in \{1, \dots, n\}} \sum_{i=0}^{k-1} \binom{n}{i} p_0^i (1-p_0)^{n-i} \leq \alpha$$
+
+The second hypothesis is rejected if $m > c_{1-\alpha}$ and the third one if $m < d_{\alpha}$ .
+
+An alternative way may be appropriate if large numbers of samples are available (large means $np(1-p) \geq 9$ is fulfilled). In this case, the test statistic:
+
+$$Z = \frac{m - np_0}{\sqrt{np_0(1-p_0)}}$$
+
+can be applied. In this case:
+
+- the first hypothesis is rejected, if $|Z| > u_{1-\alpha/2}$ ;
+- the second one, if $|Z| > u_{1-\alpha}$ ; and
+- the third one, if $|Z| < u_{1-\alpha}$ .
+
+#### 11.2.7.1.4 Distribution-free tests for location
+
+If the location for two sets of random variables shall be compared, but there is not enough knowledge for a distributional assumption, distribution-free test may be applied.
+
+##### 11.2.7.1.4.1 Sign tests
+
+In the case of paired samples, the differences between both measurements for each unit are again computed as $D_i = X_i - Y_i$ . If both distributions have the same location, the probability of $X_i < Y_i$ should equal the probability of $X_i > Y_i$ and both should equal 0.5. Based on this consideration, the following tests may be carried out.
+
+- Test for $H: P(X_i > Y_i) = P(X_i < Y_i) = 0.5$ vs. $A: P(X_i > Y_i) \neq 0.5$ .
+- Test for $H: P(X_i > Y_i) \leq 0.5$ vs. $A: P(X_i > Y_i) > 0.5$
+- Test for $H: P(X_i > Y_i) \geq 0.5$ vs. $A: P(X_i > Y_i) < 0.5$
+
+In all cases, the test statistic $T$ is given as the number of positive differences $D_i$ . This test statistic is a binomial random variable with $p = P(X_i > Y_i)$ . Therefore, all of the above stated hypotheses are tested by applying a binomial test as described in more detail in clause 11.2.7.1.3.3.
+
+##### 11.2.7.1.4.2 Sign rank test
+
+For the same situation, another kind of test, namely the sign rank test, may be preferable if the distribution of differences is symmetric around some value $\delta$ , that is $P(D_i \leq \delta - a) = P(D_i \geq \delta + a)$ for all real numbers $a$ . In comparison to the previous clause, the sign rank test not only uses the signs of the differences between both measurements, but also the absolute values in terms of their ranks.
+
+For each of the following hypotheses, the test statistic $T = \sum_{i=1}^n V_i R(|D_i|)$ with $V_i = 1$ , if $D_i > 0$ and $V_i = 0$ otherwise and $R(\cdot)$ the rank operator that sorts the entries and gives rank 1 to the smallest entry and rank $n$ to the largest, is used as a basis for the test decision.
+
+- Test for $H: \delta = 0$ vs. $A: \delta \neq 0$ .
+- Test for $H: \delta \leq 0$ vs. $A: \delta > 0$ .
+- Test for $H: \delta \geq 0$ vs. $A: \delta < 0$ .
+
+For the test statistic, there is a distribution with expectation $E(T) = \frac{1}{4}n(n+1)$ and the variance $Var(T) = \frac{1}{24}n(n+1)(2n+1)$ . The quantiles of the resulting distribution are given in statistical text books on nonparametric methods. However, for the case where $n \geq 20$ , the distribution of $\frac{T - E(T)}{\sqrt{Var(T)}}$ may be approximated by a standard normal distribution.
+
+#### 11.2.7.1.4.3 Wilcoxon rank sum test
+
+In contradiction to both tests explained before, the rank sum test suggested by Wilcoxon is used for independent samples. It assumes that both samples come from the same kind of distribution, but with a shifted location, that is $X_1, \dots, X_n$ and $Y_1, \dots, Y_m$ ( $n \leq m$ ) are independent and have continuous distribution functions $F_X$ and $F_Y$ respectively. These are shifted by $\delta$ , i.e., $F_X(x) = F_Y(x + \delta)$ . Meaningful hypotheses are the following:
+
+- Test for $H: \delta = 0$ vs. $A: \delta \neq 0$ .
+- Test for $H: \delta \leq 0$ vs. $A: \delta > 0$ .
+- Test for $H: \delta \geq 0$ vs. $A: \delta < 0$ .
+
+In this situation, for instance a rejection of the third hypothesis, i.e., $\delta < 0$ would imply that the location of the second distribution function is significantly smaller, that is the $y$ -values are in general smaller than the $x$ -values.
+
+For the above tests, a sensible test statistic is derived by combining both data sets to one sample and computing ranks by ordering the values according to their size. The test statistic $T$ is now given by the sum of all ranks for values from the first sample, i.e., the $x$ -values. For this test statistic, expectation and variance are given by:
+
+$$E(T) = \frac{1}{2}n(n+m+1)$$
+
+and
+
+$$Var(T) = \frac{1}{12}nm(n+m+1)$$
+
+respectively. Again, exact critical values for these tests are not easy to derive, but approximations exist. If $n, m \geq 4$ and $n+m \geq 30$ , the distribution of $\frac{T - E(T)}{\sqrt{Var(T)}}$ may be approximated by a standard normal distribution.
+
+#### 11.2.7.2 Confidence interval
+
+In contrast to point estimators where a single number is used to summarize measurement data (see methods for estimating moments or quantiles in clause 11.2.5), confidence intervals describe an
+
+interval that covers the true parameter value with a certain probability. Usual probability measures are in the 90 per cent range. For example, a confidence interval represents the interval in which the mean of the underlying distribution lies with a probability of 95 per cent or with a probability of 99 per cent.
+
+Confidence intervals are related to statistical tests in the sense that a confidence interval with a given confidence level, for instance 95 per cent contains all confidence levels denoted by $1-\alpha$ , where $\alpha$ corresponds to the Type I error level for tests.
+
+As a rule of thumb, number of samples within a measurement campaign correlates with the reliability of results. In other word, the higher the number of collected samples, the more precise and trustworthy the results are.
+
+The computation of confidence intervals depends heavily on the assumed kind of distribution. In the following, the computation of confidence intervals is described for the binomial and the normal (Gaussian) distribution.
+
+### 11.2.7.2.1 Binomial distribution
+
+This clause defines how to compute a confidence interval to the level $1-\alpha$ for $p$ for a binomial distribution.
+
+At first, the computation of a confidence interval $[p_1; p_2]$ according to the binomial distribution depends on the number of tests $n$ which are executed to determine $p$ .
+
+- If the condition $n \cdot p \cdot q \geq 9$ is fulfilled, the binomial distribution can be approximated by the Normal distribution which eases the computation of the according confidence interval.
+
+The values for $p_1$ and $p_2$ are then given by:
+
+$$p_1 = \frac{2m + u_{1-\frac{\alpha}{2}}^2 - u_{1-\frac{\alpha}{2}} \cdot \sqrt{u_{1-\frac{\alpha}{2}}^2 + 4m\left(1 - \frac{m}{n}\right)}}{2\left(n + u_{1-\frac{\alpha}{2}}^2\right)}$$
+
+$$p_2 = \frac{2m + u_{1-\frac{\alpha}{2}}^2 + u_{1-\frac{\alpha}{2}} \cdot \sqrt{u_{1-\frac{\alpha}{2}}^2 + 4m\left(1 - \frac{m}{n}\right)}}{2\left(n + u_{1-\frac{\alpha}{2}}^2\right)}$$
+
+with the known parameters $m$ and $n$ from clause 11.2.6.3.2 (Binomial distribution). The term $u_{1-\frac{\alpha}{2}}$ represents the $1-\frac{\alpha}{2}$ quantile of the standard normal distribution $N(0,1)$ . Some examples for different confidence levels $\alpha$ and their according $u_{1-\frac{\alpha}{2}}$ quantile values are given in the following table.
+
+| Confidence level
$1-\alpha$ | $\alpha$ | Term $1-\frac{\alpha}{2}$ | Quantile $u_{1-\frac{\alpha}{2}}$ |
+|--------------------------------|----------|---------------------------|-----------------------------------|
+| 0.9 ( $\approx 90\%$ ) | 0.1 | 0.95 | 1.6449 |
+| 0.95 ( $\approx 95\%$ ) | 0.05 | 0.975 | 1.96 |
+| 0.96 ( $\approx 96\%$ ) | 0.04 | 0.98 | 2.0537 |
+| 0.97 ( $\approx 97\%$ ) | 0.03 | 0.985 | 2.1701 |
+| 0.98 ( $\approx 98\%$ ) | 0.02 | 0.99 | 2.3263 |
+
+| Confidence level
$1-\alpha$ | $\alpha$ | Term $1-\frac{\alpha}{2}$ | Quantile $u_{1-\frac{\alpha}{2}}$ |
+|--------------------------------|----------|---------------------------|-----------------------------------|
+| 0.99 ( $\approx 99\%$ ) | 0.01 | 0.995 | 2.5758 |
+| 0.999 ( $\approx 99.9\%$ ) | 0.001 | 0.9995 | 3.2905 |
+
+The quantile values can be taken from tabulated quantile values of the standard normal distribution respectively the cumulated distribution function of this distribution.
+
+- If the previous condition is not fulfilled, the confidence interval has to be computed with regard to the binomial distribution itself. In this case, the parameters $p_1$ and $p_2$ are called "Pearson-Clopper values". In detail, the values $p_1$ and $p_2$ represent interval boundaries which fulfil the relations:
+
+$$P(X \geq m) = \sum_{k=m}^n \binom{n}{k} p_1^k (1-p_1)^{n-k} = \frac{\alpha}{2}$$
+
+$$P(X \leq m) = \sum_{k=0}^m \binom{n}{k} p_2^k (1-p_2)^{n-k} = \frac{\alpha}{2}$$
+
+Using the relation between the binomial distribution and the $F$ distribution with $2 \times (x-1)$ , $2 \times (n-x)$ degrees of freedom (see clause 11.2.6.2.3):
+
+$$P(X < x) = 1 - P\left(F \leq \frac{n-x}{x+1} \cdot \frac{p}{1-p}\right)$$
+
+The Pearson-Clopper values can be determined as:
+
+$$p_1 = \frac{m \cdot F_{2m, 2(n-m+1); \frac{\alpha}{2}}}{n-m+1 + m \cdot F_{2m, 2(n-m+1); \frac{\alpha}{2}}}$$
+
+$$p_2 = \frac{(m+1) \cdot F_{2(m+1), 2(n-m); 1-\frac{\alpha}{2}}}{n-m + (m+1) \cdot F_{2(m+1), 2(n-m); 1-\frac{\alpha}{2}}}$$
+
+$F_{n_1, n_2; \gamma}$ represents the $\gamma$ quantile of a $F$ distribution with degrees of freedom $n_1$ and $n_2$ which are tabulated in the literature. An approximation for $\gamma$ quantiles of the $F$ distribution is given in clause 11.2.6.2.3.
+
+### 11.2.7.2.2 Normal (Gaussian) distribution
+
+In the normal distribution, confidence statements depend on the composition of known and unknown parameters. This means that different computations have to be applied if mean value and/or variance of the distribution are known. If the parameters are not known, they can be estimated by empirical values (see clause 11.2.5). Furthermore, confidence statements can be made related to the expected value, to the variance and to the standard deviation.
+
+To sum up, the estimated empirical values of the normal distribution are:
+
+- Empirical mean $\bar{x} = \frac{1}{n} \sum_{i=1}^n x_i$ , where $x_i, i=1, \dots, n$ are the sample values.
+- Empirical variance $s^2 = \frac{1}{n-1} \sum_{i=1}^n (x_i - \bar{x})^2$ .
+
+- Empirical standard deviation $s = \sqrt{\frac{1}{n-1} \sum_{i=1}^n (x_i - \bar{x})^2}$ .
+
+Based on these expressions, the following terms are applied to estimate confidence intervals for the mean value, the variance and the standard deviation of a Normal distribution:
+
+- Confidence interval for mean value $\mu$ if variance $\sigma^2$ is known:
+
+$$\left[ \bar{x} - u_{1-\frac{\alpha}{2}} \cdot \frac{\sigma}{\sqrt{n}} ; \bar{x} + u_{1-\frac{\alpha}{2}} \cdot \frac{\sigma}{\sqrt{n}} \right]$$
+
+- Confidence interval for mean value $\mu$ if variance $\sigma^2$ is unknown:
+
+$$\left[ \bar{x} - t_{n-1;1-\frac{\alpha}{2}} \cdot \frac{s}{\sqrt{n}} ; \bar{x} + t_{n-1;1-\frac{\alpha}{2}} \cdot \frac{s}{\sqrt{n}} \right]$$
+
+- Confidence interval for variance $\sigma^2$ if mean $\mu$ is known:
+
+$$\left[ \frac{\sum_{i=1}^n (x_i - \varpi)^2}{\chi_{n;1-\frac{\alpha}{2}}^2} ; \frac{\sum_{i=1}^n (x_i - \varpi)^2}{\chi_{n;\frac{\alpha}{2}}^2} \right]$$
+
+- Confidence interval for variance $\sigma^2$ if mean $\mu$ is unknown:
+
+$$\left[ \frac{(n-1)s^2}{\chi_{n-1;1-\frac{\alpha}{2}}^2} ; \frac{(n-1)s^2}{\chi_{n-1;\frac{\alpha}{2}}^2} \right]$$
+
+- Confidence interval for standard deviation $\sigma$ if mean $\mu$ is unknown:
+
+$$\left[ s \cdot \sqrt{\frac{n-1}{\chi_{n-1;1-\frac{\alpha}{2}}^2}} ; s \cdot \sqrt{\frac{n-1}{\chi_{n-1;\frac{\alpha}{2}}^2}} \right]$$
+
+- Confidence interval for standard deviation $\sigma$ if mean $\mu$ is known:
+
+$$\left[ \sqrt{\frac{\sum_{i=1}^n (x_i - \varpi)^2}{\chi_{n;1-\frac{\alpha}{2}}^2}} ; \sqrt{\frac{\sum_{i=1}^n (x_i - \varpi)^2}{\chi_{n;\frac{\alpha}{2}}^2}} \right]$$
+
+### 11.2.7.3 Required sample size for certain confidence levels
+
+In this clause, the relationship between the number of acquired measurement samples and the resulting confidence level is in the focus.
+
+Whereas in the usual measurement chain at first the samples are collected and afterwards the confidence level is determined, in some situations the reverse procedure may be necessary. For example, during a measurement period there may exist preconditions which require a certain confidence level which should be reached during the measurements. The unknown parameter is the number of measurement samples which have to be collected to reach this confidence level and must be determined in advance.
+
+Tables of required sample size depending on desired confidence levels are given in clause IX.5. The tables are based on the binomial distribution and the corresponding Pearson-Clopper expressions. Due to this fact, they are available and valid for rather small sample sizes like they occur if manual tests are executed.
+
+The tables provide two kinds of information:
+
+- The limits and the range of the confidence interval of the mean for an increasing number of samples whereas the estimated rate is constant.
+- The range ("span") of the confidence interval of the mean for a varying estimated rate whereas the number of samples is constant.
+
+Based on this, one can state in advance the maximum span of the confidence interval based on the number of samples which should be gathered.
+
+### **11.3 Visualization techniques**
+
+In this clause, some useful visualization techniques are presented. This should not be considered as a complete overview over all possible methods, but provides some standard techniques and some non-standard alternatives.
+
+In the following, a distinction is made between the static and the dynamic data. By static data, variables do not change systematically within the time period under consideration, i.e., which are not subject to seasonal or daily influences. Dynamic data on the contrary are data which vary systematically over the time. Examples are usage data that show a typical curve with high usage during the day (in particular in the afternoon) and low usage at night.
+
+#### **11.3.1 Visualization of static data**
+
+Visualization techniques for static data assume that the underlying distribution does not change over the considered time period and try to give a compressed overview over this distribution.
+
+##### **11.3.1.1 Histograms**
+
+Histograms compress the information by building classes and counting the number of data values falling into each of the specified classes. The main idea is to represent each class by a bar with area equivalent to the portion of data values included. An example is given in Figure 11-18.
+
+Histograms can be viewed as density estimators since the area of the visualized bars adds up to one, smoothed density estimation curve can also be applied as available in most of the common statistical analysis computer packages. The two plots of example 1 in Figure 11-18 with different bar width illustrate the concept of histograms. Here one bar in the first plot contains the same number of data values than five successive bars in the second plot, therefore the height of one bar in plot one is given by the mean height of the five corresponding bars in plot two. Histograms even allow bar heights greater than one, if the bar width is small, respectively.
+
+
+
+Three histograms illustrating different data distributions and bin widths. Histogram Example 1 (left) shows MMS E2E-Delivery Time with a bar width of 50 sec, resulting in a sparse distribution. Histogram Example 1 (middle) shows the same data with a bar width of 10 sec, revealing a more detailed right-skewed distribution. Histogram Example 2 (right) shows normal data with mean m=6 and standard deviation s=2, forming a symmetric bell-shaped curve.
+
+**Figure 11-18 – Examples of histograms**
+
+**11.3.1.2 Barplots**
+
+Barplots are suitable for ordinal samples and to visualize the total or relative number of elements from a sample with different values of a characteristic of interest. Barplots are used if the distribution of users to groups with different business states or of trouble tickets with different priorities or other examples of ordinal samples are to be visualized. Since for ordinal samples, the differences between groups are not to be interpreted in a numerical sense, the main difference in comparison to histograms is that the widths of the bars do not have any meaning, only the height corresponds to the total or relative number of elements represented by each bar. Moreover, commonly gaps are left between the bars, to illustrate that ordinal samples are visualized. Examples are given in Figure 11-19 where months and priorities are used as example units.
+
+
+
+Three barplots showing different categorical data. Barplot Example 1 (left) shows the number of trouble tickets per month from January to August. Barplot Example 1 (middle) is a stacked barplot showing trouble tickets per month, split into two sources (Source 1 and Source 2). Barplot Example 2 (right) shows the number of trouble tickets by priority level (P1, P2, P3, P4), with P4 being the most frequent.
+
+**Figure 11-19 – Examples of barplots**
+
+**11.3.1.3 QQ-Plots**
+
+An important tool for checking the normality assumption is the so called quantile-quantile-plot (QQ-Plot). This plot compares the quantiles of two distributions in a scatter plot. In particular the theoretical quantiles of the normal distribution may be compared to empirical quantiles from a sample of interest, but also any other distributional assumptions can be checked, respectively.
+
+In case of a normal QQ-Plot, theoretical quantiles can be taken from the standard normal distribution. The points of the resulting scatter plot should then fall on a straight line with slope corresponding to the empirical standard deviation of the sample. Figure 11-20 gives three example normal QQ-Plots for normal and non-normal samples.
+
+In the first plot, the sample is in fact normal and the normal QQ-Plot also supports the assumption of normal data. For both other plots, non-normal data are simulated to visualize the normal QQ-Plot in
+
+cases where the assumptions are violated. In the second example, the entire distribution disagrees with the normal assumption while in example three, only the right tail of the distribution does not agree with the normality assumption.
+
+
+
+Figure 11-20: Three Normal QQ-Plots. Example 1 shows normal data (m=6, s=2) with points following a red line and a slope of 2.04. Example 2 shows exponential data (l=1/6) with points curving away from the line and a slope of 5.25. Example 3 shows t-distributed data (10 df) with points following the line until the right tail, where they curve up, with a slope of 2.31.
+
+Figure 11-20 – Examples of normal QQ-Plots
+
+### 11.3.1.4 Boxplots
+
+Boxplots, as the name suggests, consist of a box and some additional shapes called whiskers. These visualize the data information compressed in only a few numbers based on quantiles of the empirical distribution. The end-points of the box are given by the 25% and 75%-quantile (also called quartiles), the horizontal line is given by the median of the data (50%-quantile). Therefore the box contains 50 per cent of the given data. The whiskers (in the example plots represented by dotted lines) extend to the most extreme data point which is no more than 1.5 times the interquartile range (between the 25%-quantile and the 75%-quantile) from the box. All data points outside this interval are individually added and may be viewed as outliers. Figure 11-21 gives some boxplot examples.
+
+
+
+Figure 11-21: Three Boxplot examples. Example 1 shows normal data (m=6, s=2) with a box from ~5 to ~7.5, median at ~6, and whiskers from ~1 to ~11. Example 2 shows exponential data (l=1/6) with a box from ~2 to ~7, median at ~3, and whiskers from ~1 to ~35. Example 3 shows t-distributed data (10 df) with a box from ~5 to ~8, median at ~6, and whiskers from ~1 to ~15.
+
+Figure 11-21 – Boxplot examples
+
+### 11.3.2 Visualization of dynamic data
+
+For dynamic data, visualization techniques should take the dynamic aspect into account. This can be done by visualizing single data points or by using aggregated data values or summary statistics like the mean, respectively. In addition, visualizations as introduced for static data can be compared over time. Boxplots as described in clause 11.3.1.4 are an adequate tool for characterizing changes over time and will be addressed in clause 11.3.2.2.
+
+If summary statistics are applied, a chronological classification of the data is needed. This can be done by summarizing a given number of succeeding data points or by summarizing data of a given time period like an hour or a day. However, data within a time period or group should be as
+
+homogeneous as possible, i.e., changes of the parameter of interest should not be hidden by large classification groups for instance due to long time intervals.
+
+### 11.3.2.1 Line diagrams
+
+Line diagrams may be based on single data points or values of a summary statistic like the mean. They only provide a visual comparison of the data points over time without any kind of assessment. This can be achieved by adding control limits yielding control charts as will be described in clause 11.6.2. In Figure 11-22, examples of line diagrams are given. If the measurements are not equidistant in time, points of measurement should be specified by points in addition to the connecting line.
+
+
+
+Figure 11-22: Examples of line diagrams. It contains three subplots: 'Line Diagram Example 1' showing 'Mean Recharging Delay (sec)' over time (12h to 6h); 'Line Diagram Example 2' showing 'non-accessibility (in %) per hour' over time (0h to 24h); and 'Line Diagram Example 3' showing 'number of probing trials (specific service) per hour' over time (0h to 24h). Each plot has a line connecting data points and a shaded area representing a confidence or control interval.
+
+Figure 11-22 – Examples of line diagrams
+
+### 11.3.2.2 Temporal changing boxplots
+
+Instead of a single summary statistic, boxplots may be used as a summarizing tool and visualized and compared over time. Boxplots are not only appropriate for comparing empirical distributions over time, but also for unordered groups like the comparison of delay or delivery times for different service providers or vendors. These boxplots are then called parallel boxplots. Examples for both cases are given in Figure 11-23.
+
+
+
+Figure 11-23: Examples of parallel boxplots. It contains three subplots: 'Parallel Boxplots, Example 1' showing 'SMS E2E-Delivery time (probing trials) per hour (sec)' over time (0h to 18h); 'Parallel Boxplots, Example 2' showing 'reaction time by month (minutes)' for Jan, Mrz, Mai, and Jul; and 'Parallel Boxplots, Example 3' showing 'MMS E2E Delivery time (sec) by service provider' for P1, P2, and P3. Each plot displays a series of boxplots with lines connecting corresponding quantiles across the groups.
+
+Figure 11-23 – Examples of parallel boxplots
+
+### 11.3.2.3 MMQ-Plots
+
+Median-mean-quantile plots (MMQ-Plots) visualize the serial of each of the three statistics median, mean and 95%-quantile over time in a common plot. The 95%-quantile characterizes the upper tail of the empirical distribution, while mean and median as measures for location allow conclusions
+
+about outliers which will only affect the mean due to its non-robustness. Examples of MMQ-Plots are given in Figure 11-24.
+
+
+
+Figure 11-24 displays three MMQ-Plots illustrating temporal behavior:
+
+- MMQ-Plot Example 1:** SMS E2E-Delivery time (probing trials) per hour (sec). The x-axis shows hours from 0h to 18h. The y-axis ranges from 0 to 50. The Q95 line (red) shows high variability, peaking around 50 sec at 15h. The Mean (dashed grey) and Median (solid blue) lines are relatively stable, staying below 20 sec.
+- MMQ-Plot Example 2:** reaction time by month (minutes). The x-axis shows months from Jan to Jul. The y-axis ranges from 0 to 80. The Q95 line (red) shows significant peaks, reaching about 75 minutes in June. The Mean (dashed grey) and Median (solid blue) lines are more stable, with the Median around 20 minutes.
+- MMQ-Plot Example 3:** delay (minutes) per calendar week. The x-axis shows weeks from 20 to 24. The y-axis ranges from 0 to 25. The Q95 line (red) shows a steady increase, reaching about 25 minutes by week 24. The Mean (dashed grey) and Median (solid blue) lines are relatively stable, with the Median around 8 minutes.
+
+Figure 11-24: Three MMQ-Plot examples showing temporal behavior. Example 1: SMS E2E-Delivery time (probing trials) per hour (sec) over 18 hours. Example 2: reaction time by month (minutes) from Jan to Jul. Example 3: delay (minutes) per calendar week from 20 to 24. Each plot shows Q95 (red), Mean (dashed grey), and Median (solid blue) lines.
+
+Figure 11-24 – Examples of MMQ-Plots and their temporal behaviour
+
+#### 11.4 Time series modelling
+
+Beneath stationary processes, on the one hand temporal changes are very interesting. On the other hand, there are many cases where an appropriate description of the changes in a system over time has to be handled. Both cases are covered by the so called time series and their appropriate methods.
+
+For example, if measurements in a mobile network are executed for a period of one month with regard to the transmitted traffic volume, a periodic behaviour will be observable. Depending on the hour of the day and on the day of the week, different traffic situations are expected. Example of a daily traffic pattern is given in Figure 11-25.
+
+
+
+Figure 11-25 shows an example of daily traffic. The graph displays a highly periodic pattern over a 24-hour period. The x-axis represents time of day, and the y-axis represents traffic volume. The pattern shows peaks during the day (approximately 08:00 to 20:00) and troughs during the night (approximately 00:00 to 06:00). The peaks are sharp and frequent, indicating high variability in traffic volume throughout the day.
+
+Figure 11-25: Example of daily traffic. A line graph showing a highly periodic traffic pattern over a 24-hour period, with peaks corresponding to daytime activity and troughs to nighttime activity.
+
+Figure 11-25 – Example of daily traffic
+
+With respect to the time series, four different main areas can be identified:
+
+- 1) Descriptive characterization
+ - This method is based on the principle of "let the data speak". A very basic procedures are applied to achieve a description of time series which is as exact and detailed as possible. Especially the method of extracting different components with respect to different time scales is presented.
+- 2) Modelling
+ - Time series are interpreted as a realization of a stochastic process which means a sequence of dependent random variables. Under the stationary assumption (i.e., the main characteristics of the process are not varying over time), methods using so called auto-regressive moving average (ARMA) processes are in the focus.
+- 3) Prognosis
+ - If it is assumed that the found stochastical model is valid, it is possible to state the future behaviour.
+- 4) Monitoring
+ - Methods in this area are used to model variables which describe technical processes. The aim is to enable the controlling and monitoring of the related processes. Specialized visualization techniques, so called control charts, allow the deployment of these mechanisms in the operational realm. Their main advantage is the fact that no further detailed knowledge about statistical methods is required.
+
+#### 11.4.1 Descriptive characterization
+
+A time series is an amount of observations $x_t$ which are ordered in ascending order by a time index $t$ . The observations are interpreted as realizations of a random variable $X_t$ . In general, it is assumed that at the point of time the analysis is done, a certain history of observations is available. The history is formally described as a finite amount of parameters $N$ .
+
+$$x_1, x_2, \dots, x_N$$
+
+In a more practical manner, the observations are represented by certain measurement results which are collected over time and which are analysed in their order of occurrence. Furthermore, the observations can be distinguished according to their timely occurrence: A stochastical process can be observed at fixed points of time which leads to equally spaced intervals between the observations. Another way to observe a process is by executing permanent measurements which deliver measurement results related to some events, so called event data. In effect, the time intervals between consecutive events may vary heavily. In this case it may be appropriate to approximate the situation at fixed points of time. This allows using mechanisms which are designed for discrete time series.
+
+##### 11.4.1.1 Empirical moments
+
+Similar to the handling of one-dimensional measurement results, descriptive characteristics (clause 11.2) can be deployed to describe the main characteristics of a time series. In particular, the arithmetic mean value or the variance and the standard deviation are addressed by this issue.
+
+However, these global parameters of time series are only applicable if there is no systematic change in the series, the so called stationary time series. In these cases, a movement in a certain direction (i.e., a trend) is not allowed. Concerning non-stationary time series, it might be useful to fragment the series in smaller parts. Then, the partial time series can be assumed to be approximately stationary. This allows using some procedures with a local meaning which are presented in clause 11.4.1.4.
+
+Beyond this, the question arises if dependencies exist between different observations at different points of time. Corresponding to the covariance, the autocovariance function:
+
+$$c_j = \frac{1}{N} \sum_t (x_t - \bar{x})(x_{t+j} - \bar{x})$$
+
+and the autocorrelation function:
+
+$$r_j = \frac{c_j}{c_0}$$
+
+are defined to measure linear dependencies between succeeding observations of a process. Both functions are defined as functions of the temporal difference (lags) $j = -(N-1), \dots, -1, 0, 1, \dots, (N-1)$ between the observations.
+
+The graphical representation of the autocorrelation function $r_j$ is named correlogram. Correlograms are of high relevance for finding cyclic (i.e., periodic) structures in the gathered measurement data.
+
+Figure 11-26 gives an example of a correlogram.
+
+
+
+Figure 11-26 – Example of correlogram. The plot shows the autocorrelation function (ACF) for a time series. The x-axis is labeled 'Lag' and ranges from 0 to 25. The y-axis represents the autocorrelation coefficient, ranging from -0.2 to 1.0. The plot features a solid vertical line at lag 0 with a value of 1.0. Other vertical lines represent the autocorrelation at various lags. Most of these lines are within the bounds of two horizontal dashed blue lines, which indicate the threshold for statistical significance. Notable spikes occur at lag 1 (approx. 0.35), lag 7 (approx. 0.5), lag 14 (approx. 0.4), and lag 21 (approx. 0.45). Several other lags show small negative or positive values within the significance bounds.
+
+**Figure 11-26 – Example of correlogram**
+
+Furthermore, the autocovariance function again depends on the stationary character of the measurement results because its definition assumes the existence of constant mean values.
+
+#### 11.4.1.2 Decomposition of time series
+
+The reflection of the example given in the last clause shows the following: Data which is related to the behaviour of the users leads to a mixture of short-term cycles like days and long-term cycles which change on an annual basis. This means that the daily changes are overlaid by, for example, weekly changes as well as seasonal or yearly modifications.
+
+Now, the aim to reach by the decomposition of time series is the following: The time series should be decomposed to be able to identify the long-term trend of a process. The question which should be answered is: Are there any long-term movements behind the different layered cyclic processes? Especially with respect to management decisions, this information can be of a very high importance.
+
+In general, time series are based on two different assumptions:
+
+- Additive time series:
+
+$$x_t = T_t + K_t + S_t + R_t \text{ for } t = 1, \dots, n$$
+
+- Multiplicative time series:
+
+$$x_t = T_t \times K_t \times S_t \times R_t \text{ for } t = 1, \dots, n$$
+
+The different parts are in detail:
+
+- $T_t$ (*Trend*) represents the systematic long-term changes of the mean level of a time series.
+- The economic component $K_t$ includes long-term changes in the model which need not to be regular in any way. The combination of $T_t$ and $K_t$ often is concentrated in terms of a smooth component $G_t$ .
+- Cyclical changes are represented by the term $S_t$ which is the seasonal component of the process.
+- $R_t$ stands for an irregular behaviour of the process which is not known in advance. This component is assumed to be part of a random process which oscillates around the zero level.
+
+If seasonal changes occur with the same amplitude in each period, the additive time series model should be taken into account. However, if seasonal changes causes change of their amplitude with every observation period while they keep their general behaviour, the multiplicative approach may be the better choice.
+
+Figure 11-27 depicts an example of decomposition in different components.
+
+
+
+Figure 11-27: Example of decomposition in different components. The figure consists of four vertically stacked line graphs sharing a common x-axis labeled 'Time' with markers for 1960, 1970, 1980, and 1990. The top graph, labeled 'observed', shows a time series with a clear upward trend and a regular seasonal pattern, with values ranging from approximately 315 to 365. The second graph, labeled 'trend', shows a smooth, steadily increasing curve representing the long-term trend, with values ranging from approximately 320 to 360. The third graph, labeled 'seasonal', shows a highly oscillatory pattern with a constant amplitude, fluctuating between approximately -3 and 3, centered around zero. The bottom graph, labeled 'random', shows a highly irregular, noisy pattern fluctuating around zero, with values ranging from approximately -0.5 to 0.5.
+
+**Figure 11-27 – Example of decomposition in different components**
+
+In general, there is no statement on how to process a given time series in an optimal way. Therefore, different approaches or modelling types may lead to different results. Particularly, two different approaches can be distinguished:
+
+- Global component model: The time series is mapped to a global model which is valid for all clauses of the time series and which is adapted to the specific time series. The estimation of the trend component is usually done by the adaption of linear and non-linear regression models based on the method of minimized square values.
+- Local component model: In this model, the time series is split up in different clauses. For every clause, a certain local model with partial meaning can be developed. The concatenation of all the local models represents the complete time series. The trend estimation is normally done by filtering mechanisms and non-linear procedures.
+
+Both different models are discussed in the following clauses.
+
+### 11.4.1.3 Determination of the trend component
+
+The trend component describes the long-term behaviour of a process. Due to the importance that the trend component may have with regard to the management view, the choice of the appropriate model is one of the main issues. The use of an incorrect model has a far reaching influence with respect to the quality of the component model.
+
+Furthermore, wrong assumptions may lead to misinterpretations. For example, if a linear trend model is introduced, the conclusions drawn from such a model are restricted to a linear character. It is not possible to describe processes which own a more complex function with such a simple model which may result in obtaining wrong conclusions.
+
+#### 11.4.1.3.1 Trend function types
+
+Different types of trend functions are possible. All of them introduce some unknown coefficients $a_i$ which must be determined by calculation or estimation. The subsequent clauses introduce different approaches and their characteristics.
+
+##### 11.4.1.3.1.1 Linear trend function
+
+The most well-known approach to model a trend function is a linear function. It is assumed that the observations $x_t$ depend on the time index $t$ in a linear manner. This relation can be formalized by the expression:
+
+$$x_t = a_1 + a_2 t$$
+
+It is very easy to interpret this model since the sign of $a_1$ represents if the time series increases (positive sign) or decreases (negative sign) over time.
+
+##### 11.4.1.3.1.2 Polynomial trend function
+
+Extending the linear approach, the polynomial approach assumes that a time series can be described as the composition of different functions $m_i$ :
+
+$$x_t = a_1 m_1(t) + a_2 m_2(t) + \dots + a_k m_k(t)$$
+
+$m_i(t)$ are arbitrary known functions. It is important that the combination of all the single expressions $a_i m_i(t)$ is linear.
+
+A very simple approach is to define the $m_i$ functions as polynomials of rank $(i-1)$ . Then, the approach reads:
+
+$$x_t = a_1 + a_2 t + \dots + a_k t^{k-1}$$
+
+According to the theory, $p + 1$ points of a function can be perfectly approximated if a polynomial of rank $p$ is used. This means it is possible to reach a perfect approximation between model and any time series in any case. However, there are two serious disadvantages:
+
+- Resulting models are very complex and cannot be interpreted in a simple manner (compared with the basic trend model).
+- The assimilation takes only the available data into account. Therefore, it is not possible to make statements about the behaviour in the future.
+
+Both effects are considered as overfitting effects.
+
+#### 11.4.1.3.1.3 Non-linear trend models
+
+Lots of different non-linear trend models are available. Because of difficulties to describe all models in a general manner, this clause concentrates on some important cases with a very special meaning:
+
+- 1) Exponential models:
+
+$$x_t = e^{a_1 m_1(t) + a_2 m_2(t) + \dots + a_k m_k(t)}$$
+
+- 2) Power models:
+
+$$x_t = m_1(t)^{a_1} \cdot m_2(t)^{a_2} \cdot \dots \cdot m_k(t)^{a_k}$$
+
+Both models can be reduced to linear models if a logarithmic operation is applied. Then, the multiplication respective exponentiation is reduced to a simple addition.
+
+- 3) Logistic models.
+
+In many use cases, it can be assumed that natural limits exist which can be reached by a time series if observations are done over a longer period of time. For example, the growth of users in a network follows a function which is shaped like an S. In other words, these processes are constrained by saturation effects.
+
+Formally, these time series can be described the following approach:
+
+$$x_t = \frac{a_1}{a_2 + e^{-a_3 t}}$$
+
+In this case the values of the time series converge to the saturation at the value $G = a_1/a_2$ .
+
+#### 11.4.1.3.2 Trend estimation
+
+The common principle behind the different presented approaches is to determine the unknown parameters $a_i$ . Generally, this is done by estimating the minimization of a squared expression based on a difference. The difference is built by comparing the measurement value $x_t$ with the valid approximation given by the chosen approach. Afterwards, the resulting difference is squared and summed up. For the polynomial approach, the according overall expression reads:
+
+$$Q = \sum_{i=1}^N (x_t - a_1 m_1(t) + a_2 m_2(t) + \dots + a_k m_k(t))^2 \rightarrow \text{Minimum}$$
+
+Now, the task is to minimize the expression for $Q$ .
+
+To solve the minimization problem, partial derivatives are calculated. In detail, $Q$ is derived with respect to each of the parameters $a_i$ :
+
+$$\frac{\partial Q}{\partial a_i} = 0, i = 1, \dots, n$$
+
+A system of so called normal equations is the result of this calculation. Under the assumption of linear independency of the different functions $m_i$ , a closed solution exists so that the different parameters $a_i$ can be identified.
+
+Related to non-linear models, the minimization approach leads to normal equations that cannot be solved explicitly. A further leading approach which might solve these problems is based on linear approximations and is called the Gauss-Newton procedure. Additional information can be found in [b-Bates].
+
+Figures 11-28 and 11-29 show respectively examples of a linear model and a polynomial model.
+
+
+
+A scatter plot showing a set of data points that follow a clear linear upward trend. The points are distributed around a straight line, indicating a linear relationship between the variables.
+
+Scatter plot showing data points following a linear trend.
+
+**Figure 11-28 – Example of a linear model**
+
+
+
+A scatter plot showing a set of data points that follow a polynomial trend. The points initially decrease sharply and then level off, forming a curve that is characteristic of a polynomial function.
+
+Scatter plot showing data points following a polynomial trend.
+
+**Figure 11-29 – Example of a polynomial model**
+
+### **11.4.1.3.3 Transformation of time series by filtering**
+
+Besides the determination of global trends the identification of local trends within the time series is important. The identification of local trends corresponds to the smoothing of a time series by applying filtering functions. One main advantage of this procedure lies in the fact that low-ordered polynomials already lead to acceptable results. This simplification reduces the required computational power.
+
+On the other hand, the main disadvantage of this method is caused by the possibility to get a high number of describing parameters without finding an easy to handle closed solution or model description. In other words, the outcome of this approach may be a smoothed time series, but no model description.
+
+#### 11.4.1.3.3.1 Linear filters
+
+A very simple approach to reach smoothing effects is the application of a sliding window to a time series. This corresponds to the calculation of a moving average. In general, this approach can be formally described as follows:
+
+A linear filter $L$ is a transformation of a time series $x_t$ into a time series $y_t$ with respect to the relation:
+
+$$y_t = Lx_t = \sum_{i=-q}^s a_i x_{t-i} \quad i = s+1, \dots, N-q$$
+
+Where, $(a_{-q}, \dots, a_s)$ symbolize different weights.
+
+The simplest approach is done by "simple moving average". According to the notation given above, it reads:
+
+$$a_i = \frac{1}{2q+1}, i = -q, \dots, q$$
+
+The smoothing effect increases with the number of values taken into account which is the case for increasing values of $q$ .
+
+Reducing the condition for the weighting parameters of the filter to the standardization $\sum a_i = 1$ , it is possible to prove that the local approximation based on polynomials is equivalent to the filtering method:
+
+- 1) The simple moving average is the same as a local trend estimation of the data $(x_{t-q}, \dots, x_{t+q})$ .
+- 2) The filter represented by the polynomial:
+
+$$y_t = \frac{1}{35}(-3x_{t-2} + 12x_{t-1} + 17x_t + 12x_{t+1} - 3x_{t+2})$$
+
+represents a local trend estimation according to the squared minimization approach which is based on a polynomial of second order.
+
+Figures 11-30 to 11-32 show examples for filtering with linear and polynomial filters.
+
+
+
+A line graph showing a noisy signal with several sharp peaks and troughs. The signal fluctuates around a central horizontal axis. There is a prominent sharp peak near the center and a sharp trough on the left side.
+
+**Figure 11-30 – Example of linear filter with $q = 7$**
+
+
+
+A line graph showing a noisy signal, similar to the one in Figure 11-30, but with slightly different peak and trough amplitudes. The overall shape of the signal is more compressed vertically compared to the previous figure.
+
+**Figure 11-31 – Example of linear filter with $q = 20$**
+
+
+
+A line graph showing a highly volatile time series with several sharp peaks and troughs, illustrating the result of a polynomial filter.
+
+**Figure 11-32 – Example of polynomial filter**
+
+### 11.4.1.3.3.2 Exponential filters
+
+Linear filters always use a constant number of weights. Furthermore, a different approach can be interesting which takes into account that older values may be less interesting than newer ones. This is realized by decreasing the weights of older values whereas newer values lead to a higher weighting and is known as an exponential approach. This approach reads in recursive description:
+
+$$y_{t+1} = (1 - a) \sum_{i=0}^{\infty} a^i x_{t-i}$$
+
+and is equivalent to the formula:
+
+$$y_{t+1} = ax_t + (1 - a)y_t$$
+
+Both expressions are stated in such a way that they can be read as a prediction for the next point of time.
+
+From this equation it can be seen that exponential smoothing also overcomes another limitation of moving averages: older values are weighted with decreasing weights. That is, since $a$ is a number between 0 and 1, the weights $[a, a(1-a), a(1-a)^2, \text{etc.}]$ show a decreasing magnitude. These are the reasons why exponential smoothing has gained such wide acceptance as a forecasting method.
+
+Rearranging the terms in the above equation would result:
+
+$$y_{t+1} = y_t + a(x_t - y_t)$$
+
+In this form, the new forecast is simply the old forecast plus $a$ times the error in the old forecast ( $x_t - y_t$ ). It is evident that when $a$ has a value close to 1, the new forecast will include a substantial adjustment for any error that occurred in the preceding forecast. Conversely, when $a$ is close to 0, the new forecast will not show much adjustment for the error from the previous forecast. Thus, the effect of a large and a small $a$ is analogous to the effect of including a small number of observations in computing a moving average versus including a large number of observations in a moving average.
+
+Figures 11-33 and 11-34 show examples of exponential filters with $a = 0.1$ and $a=0.5$ respectively.
+
+
+
+Figure 11-33: A line graph showing a time series with significant noise and a few sharp spikes. The signal is relatively flat with high-frequency oscillations around a central value. A very tall, narrow spike is visible in the upper right quadrant.
+
+**Figure 11-33 – Example of exponential filter with $\alpha = 0.1$**
+
+
+
+Figure 11-34: A line graph showing the same time series as Figure 11-33, but with a different filtering effect. The signal appears slightly smoother than in Figure 11-33, but the sharp spikes are still prominent. The overall shape of the data is identical to the previous figure.
+
+**Figure 11-34 – Example of exponential filter with $\alpha = 0.5$**
+
+#### **11.4.1.4 Seasonal component**
+
+Data series which are related to the user's usage essentially contain seasonal figures, which imply cyclical fluctuations with regular characteristics. Interesting intervals in this area are yearly, monthly and daily periods.
+
+Based on the two following views, it may be possible to eliminate the seasonal influences in the available data. Considering practical issues, the latter view is mostly preferred.
+
+- Retrospective view: How would the data have been in the past if no seasonal influences were overlaid?
+- Prospective view: What is the long term tendency with respect to the smooth component?
+
+As an example of all the different possible procedures, the so called "mean phase" procedure is explained. This procedure is one of the easiest of the available procedures. It is suitable to achieve the elimination of a fixed seasonal component of a time series without a trend component. This means
+
+within the data of the time series no trend component is allowed. It must have been removed before by a trend elimination mechanism.
+
+The procedure can be subdivided into four different steps. Generally, it is assumed that the time series $x_1, \dots, x_N$ can be separated in different parts, the so called phases $p$ , each with a length of $n$ data elements. Formally, this relation is given by:
+
+$$(x_t), t=1, \dots, N \rightarrow (x_{i,j}), i=1, \dots, p, j=1, \dots, n$$
+
+The first index $i$ describes the number of the segment or the phase whereas the second index $j$ represents the consecutive number within this phase. For example, if a data series contains data of a period of 5 years on a monthly basis, it can be described by the parameters $p = 5$ (representing 5 phases, each for one year) and $n = 12$ (representing the 12 months within each year).
+
+The following calculations implement the already mentioned four steps to achieve data without underlying seasonal impacts:
+
+- 1) Calculation of the average of different phases:
+
+$$\bar{x}_j = \frac{1}{p} \sum_{i=1}^p x_{i,j}$$
+
+- 2) Calculation of the total average:
+
+$$\bar{x} = \sum_{j=1}^n \bar{x}_j$$
+
+- 3) Calculation of seasonal indices (seasonal factors):
+
+$$s_j = \frac{\bar{x}_j}{\bar{x}}$$
+
+Related to the last mentioned calculation, the averaged phases $\bar{x}_j$ are set into relation to the total average $\bar{x}$ . In the example, the average of January is for example divided by the total average. This step is done for all of the different monthly averages. If January data is much above average, then $s_1 > 1$ is the result, and if January data are much below average, $s_1 < 1$ will be the result.
+
+- 4) Calculation of seasonally adjusted values:
+
+$$y_{i,j} = \frac{x_{i,j}}{s_j}, i=1, \dots, p, j=1, \dots, n$$
+
+This step concludes the basic calculation scheme related to the determination of the seasonal component.
+
+## 11.5 Data aggregation
+
+Depending on the objective, i.e., for monitoring or reporting purposes, different types of aggregation may be of interest. First, different granularities in time and space may be needed. Second, weighting of data may be considered for instance to generate the QoS perceived by the user. This may be more or less straightforward, if the smallest time granularity is evenly spaced and the full information at any time point or interval is available, i.e., there are no missing data. However, data aggregation becomes more challenging if the event data are considered, like data from active probing systems, or if data are missing and substitution algorithms are needed for defining meaningful results at higher aggregation levels.
+
+In the following, after presenting some basic aggregation operators, different data sources and corresponding structures are distinguished, temporal and spatial aggregation levels are defined and estimation methods for incomplete data are suggested. Based on a summary of desirable attributes of
+
+aggregation procedures, an aggregation strategy is suggested and discussed. Subsequently, weighted aggregations are motivated and weighting methods are introduced.
+
+### 11.5.1 Basic data aggregation operators
+
+Most common data aggregation operators are **sums** and **means**. Sums are applied if the total number of events, usage, etc., within a given time period is of interest, while means are commonly applied if some kind of averaging is needed. However, in particular for data out of time-interval measurements, means may not lead to interpretable and convincing aggregation results, therefore other summarizing statistics like the minimum, maximum, median and other quantiles of data are also used as aggregation operators. For quality of services measures like accessibilities, retainabilities and so on, ratios in the sense of fractions are used.
+
+The combination of aggregation operators in a row might raise problems, even if the same operator is used at each single step. If the data basis is complete, the combination of sums on different aggregation levels, that is summing sums of lower levels for a higher level aggregation result, ensures a meaningful interpretation. If the considered time intervals are equidistant in addition, the same holds true for the combination of means on different aggregation levels. Minimum and maximum operators are also examples where this kind of combination is possible and meaningful. However, for other quantiles like the median or Q95, it is not recommended to base aggregations on higher levels on the results of lower aggregation levels, since for instance the median of subgroup-medians is not guaranteed to be near the overall median. Aggregation methods for fractions will be discussed later.
+
+If different aggregation operators are combined one after another, the resulting values should be interpreted with great care. For instance the minimum and maximum of mean values from lower aggregation levels should not be mistaken as the range of the original data values. However, one can think of many examples where this kind of combination yields meaningful results that are of interest, for instance if different base station controllers (BSCs) are compared with regard to QoS parameters, the median or quantiles of the values for all BSCs may be used as cut-points to identify BSCs performing particularly good or bad.
+
+### 11.5.2 Data sources, structures and properties
+
+In the following, a distinction between raw data that may result from data sources with different attributes and parameters that are defined based on these raw data is made.
+
+#### 11.5.2.1 Raw data
+
+For measuring QoS, data from a number of different data sources are used. These "raw data" are based on different measurement systems with possibly different granularities, differences due to release changes of the underlying systems and so on. Therefore, raw data often come with a number of attributes that need to be taken into account for aggregation. Here performance data and event data are considered, although other data sources and types could also provide valuable information about QoS (like fault management data).
+
+In the situation that not all data are available – which is a common problem not only in mobile communications – raw data are rarely substituted or adjusted, but are stored with the full source information to allow suitable parameter definition and estimation. This is often only possible by applying all available reference knowledge, for instance which or how many cells were supposed to deliver data for a given time period.
+
+##### 11.5.2.1.1 Performance data
+
+Most performance data are given by network element (NE) counters. Due to different underlying systems or releases, these may be available in different temporal granularities, like values for 15 minutes from one system and values for hours from another system, respectively. Here basic operations are needed to enable a common data format in order to ensure that values are comparable and basic aggregations are needed for total NE counter values independent of underlying systems. In
+
+addition to results of basic aggregations, the total number of aggregated values or even additional detailed reference information needs to be stored.
+
+Examples for performance data are the number of calls per hour per cell, the total number of call minutes per quarter-hour per cell or the number of started WAP-sessions per hour per cell.
+
+#### **11.5.2.1.2 Event data**
+
+Billing data are one example of event data that may provide information about the QoS. On the other hand, results from active probing systems are also in general not equally spaced over time. This may be due to varying duration of trials, possibly depending on network performance or other influences. Also there may be reasons to do a larger number of tests regarding a specific service over a given period of time, for instance if new services are launched.
+
+Event data do not provide information of a time interval, but just a number of results, each for a corresponding point in time. To allow statements about time periods, it is either possible to use all original data points for defining and aggregating parameters for each time interval of interest, or as an alternative, relatively small time intervals have to be defined for which a first, "basic", aggregation step is carried out which then allows higher aggregations independent of the original data.
+
+#### **11.5.2.2 Key performance indicators/parameters**
+
+A particular feature of a key performance indicator (KPI or parameter) – in comparison to raw data – is given by the fact that KPIs are defined in an abstract manner, thereby allowing a common interpretation for parameters computed from different data sources and on various aggregation levels. Usually, there are two possible reasons for a parameter to be identified as a KPI, either:
+
+- the KPI is a function aggregation of different parameters; or
+- the KPI represents a very important quality measure related to the user's perspective.
+
+In the latter case, data aggregation is not necessarily implied.
+
+Parameters are defined to serve specific purposes like answering questions about QoS or performance by utilizing raw data or basic aggregations of raw data. This might also include combinations of different data by mathematical operations like ratios. Unlike raw data, parameters are independent of the underlying software releases or system vendors. One could also define them as being independent of different underlying systems, if appropriate.
+
+Parameters based on performance data are for instance the cut-of-call ratio, which is based on two different NE counters, namely the number of unintentionally terminated calls divided by the number of successful call attempts times 100%. Data from active probing systems allow the definition of parameters like the recharging-failure-ratio, SMS-E2E-failure-ratios and so on.
+
+For the definition and computation of parameters, rules for handling missing data are needed. Therefore, methods for data substitution become a major point when talking about parameter computation and aggregation and will be covered in some detail after defining aggregation hierarchies of interest.
+
+### **11.5.3 Aggregation hierarchies**
+
+Aggregation hierarchies are commonly divided into temporal and spatial aggregation hierarchies, where temporal in fact refers to different time intervals while the term spatial may also cover aggregations over items with similar properties with respect to an attribute of interest.
+
+#### **11.5.3.1 Temporal aggregation**
+
+Temporal aggregation levels that should be used for a given parameter will depend on the intended use of the parameter as well as on the raw data frequency. For most parameters, sensible aggregations levels will be some or all of the ones given in the following set:
+
+- quarter-hour;
+
+- hour;
+- day;
+- week;
+- month;
+- quarter year;
+- calendar year; and
+- business year.
+
+In addition, incomplete temporal aggregation levels can be defined, for instance cumulative values for the current week based on the daily values that are available so far. This is of particular interest for parameters that are based on ratios or mean values because these may be interpreted directly. For the interpretation of incomplete parameters that are based on sums, the number of covered time units has to be taken into account.
+
+### 11.5.3.2 Spatial aggregation
+
+Spatial aggregation originally refers to aggregation levels from the smallest possible units like radio cells (or even sectors) in mobile communications up to the entire network. This can be done from a technical point of view for instance by taking the order "cell – BSC – MSC – (branch) – entire network", or from a regional point of view by "cell – state/region – entire network".
+
+As mentioned before, the term "spatial" may also be used in the context of summarizing cells with similar properties regarding an attribute of interest, like all cells that cover motorways, or the position of a cell in terms of the surrounding area, whether it belongs to a large city, a small town or a rural area. In these cases, spatially incoherent units are aggregated.
+
+### 11.5.4 Parameter estimation methods
+
+The ideal situation of full information is rarely met in practice. Network elements or branches failing to deliver data in time are common reasons for missing data. Since in most situations missing values as parameter values are unacceptable, even if parts of the raw data are missing, data estimation methods are needed. Depending on the situation, projection, substitution or combined estimation methods are suitable.
+
+#### 11.5.4.1 Projection method
+
+The easiest method of data substitution is to project the available data to the entire time interval of interest. For example, if a fraction of 90 per cent of the expected data measuring the quality of a specific service within one hour is available and an hour-value is sought, these 90 per cent of the data are viewed as being representative for the hour of interest. If the aggregation consisted of cumulating the entries, the value achieved by the available data has to be multiplied by a factor 100/90. If aggregated values are mean (or median) values, the mean (or median) of the available data is used as an aggregated value. If minimum or maximum values (or other quantiles besides the median) are sought as aggregated values, more sophisticated estimation methods should be applied like maximum likelihood methods.
+
+Provided that a high percentage of data is available and there are no systematic reasons for missing data, the above procedure is sufficiently reasonable. However, an analysis of the times and circumstances of missing data might be of interest to identify the reasons, if missing values are becoming more frequent or appear suspect that there might be an underlying pattern.
+
+If only a low percentage of data is available for a time period of interest, for instance less than 50 per cent, the above projection procedure is more than questionable. In particular if the parameter of interest is subject to dynamic changes over time, the results may be heavily biased. As an example, consider usage data where a high percentage of missing data consists of records that should have been taken at night. If the usage is then estimated by projecting the available results, the usage is extremely
+
+overestimated. Therefore, it seems sensible to define a "critical percentage" of data that need to be available for applying the above method. This percentage should depend on the specific service which is evaluated and on the needed precision of the aggregation results.
+
+#### **11.5.4.2 Substitution method**
+
+If the estimation of parameters can or should not be based on the available data (i.e., a large number of data is missing or the data are not credible for some reason), substitution methods would apply to values from the previous time periods. This can either be done by using the last available value for the parameter of interest, which is only sensible for static parameters that are not subject to dynamic changes over time, or by using the last available value of a comparable time period with similar properties like the same day-type (weekday/Saturday/holiday or Sunday) or the same day of the week and the same time.
+
+#### **11.5.4.3 Application of estimation methods**
+
+Common problems that complicate the application of the methods suggested above are given by:
+
+- 1) Unavailability of reference data: The number of missing data is needed for deciding which method should be used and for the application of the projection method.
+- 2) Determination of values for substitution: Comparable time intervals have to be defined and substitution values may be stored in a database, which needs to be updated and maintained. In addition, calendar information about holidays/working days, weekdays and so on is needed.
+- 3) Parameters that are defined as ratios: Either numerator and denominator are estimated separately based on the available information for each part, or the ratio is estimated as a whole by using only data with information about both, numerator and denominator. In the situation of full information, there is no difference between both possibilities, in case some data are available for one part of the ratio and not for the other, both strategies yield different results.
+
+Referring to data aggregation, the question arises, at which aggregation level estimation should take place. Is it acceptable to use estimated values as a basis for higher aggregations? Data aggregation procedures combining both introduced methods are derived in the following, originating from a summary of desirable attributes of aggregation procedures.
+
+#### **11.5.4.4 Attributes of aggregation operators**
+
+Aggregation methods may be evaluated according to the following attributes:
+
+- 1) The result should be meaningful, that is as near as possible to the true value of interest. In particular, NULL-values are not sensible as a result of a higher level aggregation. In addition, all information about missing values should be used to take non-available data into account, to avoid biased parameter values. (Moreover, the variance of parameter values caused by estimation methods should be as small as possible.)
+- 2) Aggregation results should be reproducible and understandable. In particular, at higher aggregation levels no estimation procedure should be used so that results on a higher aggregation level are in accordance with values of the underlying level.
+- 3) Aggregation results should not depend on the used aggregation paths, i.e., there should be no difference of results, if spatial or temporal aggregation steps are interchanged as well as direct aggregation and aggregation with intermediate steps should not lead to different results. Independence of paths refers to aggregation calculations.
+- 4) Results should be consistent. On a given aggregation level, individual aggregation results should agree with total result, i.e., the sums for different branches should add up to the total sum for the company and so on. Consistency refers to aggregation results.
+
+- 5) The applied calculation procedures should be rather easy. This also implies independence of the past values like those from the previous time periods.
+- 6) Independence of network references like assignment of results to network elements.
+
+In general, it is not possible to meet all requirements at the same time. Easy methods may lead to non-sensible results, while methods that contain estimation procedures often rely on values from the previous time periods or network references and may be more sophisticated. In particular the requirements 1 and 5/6 are contradicting as estimation methods ignoring network references and past values will presumably often lead to worse results compared to methods that take into account all available information.
+
+One idea to combine the above requirements to a certain extent is to define a smallest temporal and spatial aggregation level for which the data are completed by estimation procedures (for missing parameter data) or basic aggregations (for event data and parameter data with different lowest levels), like per hour per cell. This yields an equally spaced "complete" data pool and therefore simplifies all further aggregation steps and in particular ensures consistency of results and independence of aggregation paths.
+
+One major disadvantage of this method is the fact that estimation procedures have to be applied on low aggregation levels which rely heavily on reference data and good substitution values or projection procedures. For parameters that are dynamic over time, time series methods as covered in clause 7 should be considered, which then implies more complicated calculation procedures for low aggregation levels and therefore might take some computation time.
+
+### 11.5.5 Weighted aggregation
+
+In many situations, in particular if the QoS perceived by the user is of interest, simple aggregations of the available information or estimated values are not very meaningful. A better approach would be to take into account, e.g., how many users are affected if a service fails. That leads to the idea of weighted aggregation, where for instance the usage can be applied for weighting, respectively. It should be noted, however, that weighted aggregation methods will in general lead to non-consistent results in the sense of property 4 from clause 11.5.4.4.
+
+#### 11.5.5.1 Perceived QoS
+
+Depending on the point of view and the corresponding intention of a parameter of interest, it appears reasonable to only consider users view instead of the network view, e.g., by taking the usage of a service into account. Depending on the applied aggregation procedure, this may have already been done implicitly. For instance, if the aggregated cut-off call ratio for a particular week is considered, different aggregation procedures imply different weightings (it is assumed that values are available hourly):
+
+- 1) If the cut-off call ratio is stored for hourly intervals and the weekly aggregation is done by averaging all values for the week of interest, no weighting is carried out and each hour viewed as being equally important. This does not correspond with the user's perspective. (Note that a geometric rather than an arithmetic mean should be applied for averaging ratios.)
+- 2) If the numerator and denominator are stored for hourly intervals and the weekly aggregation is done by first summing all entries for the numerator and denominator separately and then dividing both numbers, an implicit weighting is carried out. Since high usage time intervals contribute a larger number of call attempts than low usage intervals, thereby the user's perspective is expressed.
+
+When applying the first method, one should consider using weighted means instead of simple means. Depending on the type of parameter, weighted arithmetic means are computed according to:
+
+$$\bar{x}_w = \sum_{i=1}^n x_i w_i$$
+
+where $\sum_{i=1}^n w_i = 1$ , weighted geometric means are given by:
+
+$$\tilde{x}_w = (\prod x_i w_i)^{1/n}$$
+
+where $\prod w_i = 1$ . (For unweighted arithmetic or geometric means, all weights are given by $w_i \equiv 1/n$ or $w_i \equiv 1$ , respectively.)
+
+Weights can either be based on true or average usage curves, but also on strategic reasons or any other procedure in accordance with the aim of the analysis. An average usage curve may for instance be achieved by averaging over the past 30 working days, the past five Saturdays and the past five Sundays or holidays or by applying some kind of time series modelling and forecasting methods. Weights based on usage curves are then computed as:
+
+$$w_i = \frac{u_i}{\sum u_i} \text{ or } w_i = \frac{u_i}{\prod u_i}$$
+
+respectively, where $u_i$ is the true or estimated usage within time period $i$ .
+
+If the second method from above is applied, weighting is done implicitly with the actual usage curve. However, other problems arise in particular regarding missing data handling as mentioned in clause 11.5.4.3. For each time period, the percentage of missing data might be of interest for applying projection or substitution methods and to ensure that the cut-off call ratio does not exceed 1, e.g., the number of unintentionally terminated calls should not exceed the number of successful call attempts, one might only want to consider data pairs where both numbers are known. When using the first method, this could be avoided by estimating only on an hourly basis.
+
+Remark: For ratios, higher level aggregations are commonly achieved by applying the second method because of the implicit weighting, which is more intuitive.
+
+Data from active probing systems are generally not weighted implicitly, since probing frequencies are commonly non-representative for user behaviour. In this context, the idea of weighting might even be of importance in more than one respect.
+
+- 1) Since data from active probing systems are not equally spaced, a weighting of each trial result by the time between two trials in some way could be considered. This can either be realized by defining (rather small) time intervals for which all trials done within this interval are summarized without weighting or alternatively by computing using half of the time interval between the last trial and the current one and half of the time interval between the current trial and the next one as a basis for weighting. If such weighting is considered, an upper bound for the defined underlying intervals should be considered and strategies for the situation that the active probing system does not work or data are not delivered for a longer time period are to be thought of (estimation or NULL-values, depending on the situation and parameters of interest).
+- 2) A second – and probably more important – way of weighting results from active probing system is the usage weighting for achieving the perceived QoS as explained before.
+
+If both types of weighting are applied, combined weights are computed as $w_i = \frac{u(t_i)}{\sum u(t_i)}$ , where $u(t_i)$
+
+is the usage within time period $t_i$ assigned to probing trial $i$ according to the distribution of trials over time (1st weighting), either for a basic aggregation level for further aggregation or for the desired aggregation level directly.
+
+### 11.5.5.2 Weighted quantiles
+
+For duration values as results from active probing trials, quantiles represent meaningful aggregation results on higher aggregation levels. From the above weighting discussion the necessity of
+
+determining weighted quantiles arises. Due to the calculation of quantiles based on ordered data values, a weighting similar to those for mean values is not applicable. Instead, a **replication** algorithm could be used for computing weighted quantiles. This algorithm simply repeats each value according to an assigned weight and calculates the desired quantile of the resulting new data set. (If weights are irrational, sensible rounding is needed.)
+
+Example: The original (ordered) data set of ten MMS-E2E delivery times is given by 51, 55, 60, 61, 65, 70, 71, 72, 72, 80 seconds. These measurements have been taken at different times of the day and therefore get a weight of 1, if taken at night, 2, if taken in the morning or late in the evening and 4, if taken between noon and 8 p.m. for instance. Accordingly, weights are therefore given by 1, 4, 2, 2, 1, 4, 4, 2, 1 and 4 resulting in a data set with 25 data: 51, 55, 55, 55, 55, 60, 60, 61, 61, 65, 70, 70, 70, 70, 71, 71, 71, 71, 72, 72, 72, 80, 80, 80 and 80. Quantiles from the original and the replicated data set will in general lead to slightly different results.
+
+If weighting according to some kind of usage curve is aimed, this curve can be used as a replication function or replication curve and represents the basis for defining the needed weights. To simplify the computation, weights may be defined by identifying the minimum of the replication function $r_{\min}$ and to define the weights according to:
+
+$$w_i = \text{round} \left( \frac{r_i}{r_{\min}} \right)$$
+
+If a uniform concept for weighting of any kind of parameter is sought, the approach based on replication functions might also be used for means or non-accessibilities or other parameters of interest. Differences for instance between conventionally weighted means and means weighted by replication curves are only due to the applied rounding step for the latter approach.
+
+### 11.5.6 Additional data aggregation operators
+
+In the following, some additional data aggregation operators are covered, adding to those mentioned in clause 11.5.1, that are in some sense individual regarding their attributes and/or applications.
+
+#### 11.5.6.1 MAWD and BH
+
+In particular for network maintenance an aggregation operator of interest is the monthly average working day (MAWD). This operator can be viewed as being an aggregation result as well as a possible weighting function for other aggregation operators.
+
+The monthly average working day of a given data set is computed by filtering all data from working days within a given month first and then computing the mean value for each hour of the day over all data from corresponding hours. The result is therefore given by a vector of 24 entries, each corresponding to one hour of the day (0000 hour – 0100 hour, ..., 2300 hour – 2400 hour).
+
+Based on the MAWD, the busy hour (BH) is defined as the hour in which the MAWD vector takes its maximum value. In mathematical notation, this is $\text{argmax} (m)$ , where $m = (m_1, \dots, m_{24})^T$ is the vector resulting from applying the MAWD operator.
+
+#### 11.5.6.2 AVGn
+
+The class of parameters averaging operator regarding $n$ days (AVGn) is applied for similar reasons as the BH operator mentioned in clause 11.5.6.1. Both try to identify peaks of high usage or traffic, where the BH operator considers hours where the highest usage is observed on average, while the AVGn-operators are interested in the maximum usage or traffic for a given calendar week. The mean of the $n$ largest values realized on $n$ different days ( $n$ between 1 and 5 or 7, depending on intended use) is defined as AVGn.
+
+## 11.6 Assessment of performance indices
+
+### 11.6.1 Estimation of performance parameters based on active service probing systems
+
+End-to-end service probing systems yield valuable information about services and systems that may not be provided by the network elements alone. Active probing systems are used to view the user's perspective of the quality of service, i.e., the perceived QoS. Typical parameters that may be computed based on active probing systems are failure ratios, accessibilities and end-to-end-delivery times for a number of different services.
+
+One characteristic of active probing systems is that the tests are often done more or less equally distributed over time for utilizing the equipment as exhaustingly as possible. In this respect, they fail to reflect the user's perspective, since derogation during the day will be a lot more severe than after midnight due to lower volume of traffic for almost all services at night.
+
+From a statistical point of view, end-to-end active probing systems try to estimate the real behaviour of a service by taking a defined number of samples. Therefore, the results of measurement activities have to be interpreted as the current view on a certain service and need not necessarily represent user experience. Depending on the number of considered samples, the connection between observed measurement results and unknown real behaviour may vary.
+
+### 11.6.2 Monitoring concepts
+
+To ensure that network problems are noticed and remedied as quickly as possible, monitoring concepts based on active probing results are important tools for an efficient alarming system. Such monitoring tools may be based on control charts or other alarming rules.
+
+#### 11.6.2.1 Control charts
+
+Control charts are based on the assumption that if the service under study works properly, data gained from active probing systems follow a stable distribution with the given parameters. From previous observations, the underlying distribution may be identified and parameters have to be estimated. Control charts are now set up based on statistical considerations such that in case of network problems, i.e., the process is "out of control", an alarm is created. On the other hand, false alarms have to be avoided, that is as long as the process is "in control", no alarming should occur.
+
+Control charts generally visualize the development of a quality characteristic of interest over time similar to a line diagram as shown in Figure 11-22. Further, a target line and control and or warning limits are added to the plot. The target line represents the line around which data values are expected. Warning and control limits may be used to define identifying realizations indicating that the process is "out of control". Different types of control charts were invented for different types of data.
+
+##### 11.6.2.1.1 Shewhart control charts
+
+If data are normal or mean values are considered (central limit theorem, compare section), Shewhart charts [b-Nist 1] for normal data may be applied. In this case, the current short-term data is compared against an underlying data model which represents the long-term behaviour. Based on this model, it is possible to define the usual or "normal" situation. This is required to pay attention to unusual situations. Shewhart control charts are widely used in different sections of the industry.
+
+##### 11.6.2.1.2 CUSUM and EWMA charts
+
+Two other approaches can be used to introduce some kind of weighting into control charts. The cumulated sum (CUSUM) approach [b-Nist 2] uses sums data up over time and therefore indicates the behaviour over a greater period of time. A slightly different approach is represented by exponentially weighted moving average (EWMA) charts [b-Nist 3] where older values gain less influence than newer data does.
+
+### 11.6.2.2 Other alarming rules
+
+Furthermore, the deviation between the long-term data model and the short-term monitoring data should lead to consecutive actions if a critical state is reached. This relation is defined as "alarming rules". One example for the alarming rules is the set of the Western Electric Company (WECO) rules [b-Nist 4].
+
+### 11.6.3 Methods for evaluation of objectives
+
+Commonly objectives are formulated in terms of target values for important parameters. Then the evaluation of objectives could mean to assess to which extent these aims have been achieved within a given time-period (i.e., month or business year). If there is only one important parameter, this is a rather easy task. However, if a number of pre-defined parameters are to be combined in an overall measure and in addition different groups (i.e., branches or departments) are to be compared regarding their performance, the main issue for evaluation will be to define a common measurement scale for all parameters. This allows the combination to an overall evaluation index of some kind and thereby a comparison of groups is facilitated.
+
+In the following, two methods are described that allow the evaluation of objectives, namely the desirability approach and the loss function approach. Both approaches rely on definitions of target values and specification limits for the parameters. In this context, parameter values are denoted by $y_i, i=1, \dots, P$ and target values are denoted by $\tau_i, i=1, \dots, P$ . Specification limits are given as upper specification limit ( $USL_i$ ) and/or lower specification limit ( $LSL_i$ ) for each parameter under consideration $i = 1, \dots, P$ . (It might also be sensible to consider lower and upper target values, if the target is given as an interval instead of a numerical value.)
+
+#### 11.6.3.1 Desirability functions
+
+Desirability functions use a transformation of the values $y_i$ to the interval $[0, 1]$ based on system requirements by defining a transformation function based on target values and specification limits. Desirability functions are piecewise defined continuous linear functions where desirability values of 0 are assigned to parameter values $y_i$ outside the specification limits, realizations on target get desirability values of 1 and outcomes between target and specification limits are assigned by a linear connection or a power transformation thereof.
+
+The principle of desirability is best explained by providing example desirability functions as summarized in Figure 11-35.
+
+
+
+The figure contains three separate plots, each titled "Desirability function" with a specific example below it.
+
+- Example 1: Cut-of-Call-Ratio**: The x-axis is "Cut-of-Call-Ratio (in %)" ranging from 0 to 4. The y-axis is "desirability value" ranging from 0.0 to 0.8. A red line starts at (t, 1.0), stays at 1.0 until the upper specification limit (USL), and then decreases linearly to (USL, 0.0). The slope is labeled as $\frac{USL - y}{USL - t}$ .
+- Example 2: SMS E2E Delivery time**: The x-axis is "SMS E2E Delivery time (in sec)" ranging from 0 to 40. The y-axis is "desirability value" ranging from 0.0 to 0.8. A red line starts at (t, 1.0), stays at 1.0 until the upper specification limit (USL), and then decreases with a power transformation to (USL, 0.0). The curve is labeled with the formula $\left(\frac{USL - y}{USL - t}\right)^2$ .
+- Example 3: Time Service Factor**: The x-axis is "Time Service Factor (in %)" ranging from 50 to 100. The y-axis is "desirability value" ranging from 0.0 to 0.8. A red line starts at (LSL, 0.0) and increases with a power transformation to (t, 1.0), then stays at 1.0. The curve is labeled with the formula $\left(\frac{y - LSL}{t - LSL}\right)^2$ .
+
+Figure 11-35 shows three examples of desirability functions. Example 1: Cut-of-Call-Ratio (in %) shows a linear decrease from 1.0 at target 't' to 0.0 at the upper specification limit 'USL'. Example 2: SMS E2E Delivery time (in sec) shows a power transformation decrease from 1.0 at target 't' to 0.0 at the upper specification limit 'USL'. Example 3: Time Service Factor (in %) shows a power transformation increase from 0.0 at the lower specification limit 'LSL' to 1.0 at target 't'.
+
+Figure 11-35 – Examples of different desirability functions
+
+#### 11.6.3.2 Loss functions
+
+Loss functions in contrary evaluate a realized parameter value in terms of costs arising due to deviation from target or realization outside the specification limits. Therefore, values within the
+
+interval $[0, \infty]$ will be achieved. The main issue for the specification of loss functions is the assignment of a rising costs. The loss of earnings if services are not fully usable may be stated rather easily, but quantifying the image loss and corresponding costs might be a much more difficult task.
+
+For each parameter of interest, the arising loss for a value $y_i$ is given by $L(y_i) = c(y_i - \tau_i)^2$ or alternatively $L(y_i) = c \min((y_i - USL_i)^2, (y_i - LSL_i)^2)$ , where $c \in \mathbb{R}$ quantifies the arising cost.
+
+Mainly, normal distributed values are in the focus if loss functions are discussed. In general, the area which is covered by the lower and upper tails of the normal (or Gaussian) distribution is in the main interest. These branches violate the guaranteed specification levels USL for the upper tail and LSL for the lower tail. All values in these areas represent defects referring to the observed process. The underlying theory specifies rules how to set the limits and how to proceed with asymmetric cases. One useful hint for further research in this area is the "six sigma approach" which is wide-spread in the industry.
+
+## 12 Network based quality of service measurements
+
+### 12.1 Network measurement basics
+
+#### 12.1.1 Point of control and observation (PCO)
+
+The point of control and observation (from now on called "point of observation" or PCO) is the location where the measurement is actually performed. The location can be either inside the network or at the end-point. The measurements should be done using standardized interfaces and protocols.
+
+Possible points of observation for QoS parameters covered in this Recommendation are:
+
+- Inside nodes in the network (e.g., RNC, base station, switch, etc.)
+- Observations in the terminal:
+ - End-point test tool; or
+ - Measurements that are reported back from the terminal to the network
+
+Figure 12-1 shows points of observation, points of recording and measurement reporting.
+
+
+
+Diagram illustrating points of observation (PCO) and point of recording (POR) in a network architecture. The diagram shows a mobile terminal on the left with layers: Higher layer, RRC, RLC/MAC, and L1. It is connected to Node B, which is connected to a Network node, which is connected to another Network node. Arrows indicate the flow of data and measurement points. Callouts identify: 'PCO in mobile (can be multiple PCO)' pointing to the Higher layer; 'PCO in network node or at network node interface' pointing to the interface between Node B and the first Network node; and 'POR in a network node and data is reported from a PCO in a terminal' pointing to an arrow inside the second Network node. A small label 'E.804(14)\_F12-1' is at the bottom right.
+
+Figure 12-1 – Points of observation, points of recording and measurement reporting
+
+#### 12.1.2 Point of recording (POR)
+
+The point of recording (POR) is where the QoS parameters are recorded. The POR can be the same as the PCO or another point inside the terminal or the network. If the PCO and the POR are not the
+
+same, the measurement data must be reported from the PCO to the POR. Examples of such reporting are described in Appendix XI.
+
+## 12.2 Measuring QoS parameters in the network
+
+### 12.2.1 General overview
+
+The quality of service measurements should be done as much as possible in the same way inside the network as they are done at the end-point with a test tool. Many types of measurements can be done with the same trigger points (for instance, the reception of a certain protocol message) independent of the point of measurement, but the measurement result might differ slightly depending on where in the call or transmission chain the PCO is located.
+
+### 12.2.2 Service accessibility QoS parameters
+
+Accessibility QoS parameters reflects the ability to initiate or intentionally terminate a connection or a service. The parameters can be divided into the following groups:
+
+- **Failure parameters:** Reflects the outcome of attempts to initiate a connection or a service. For mobile-originating (MO) cases the network might sometimes be unaware of some of the attempts, and network-based parameters can be expected to give a slightly more positive view of the network condition, as compared to the corresponding endpoint-test parameters. For mobile-termination (MT) initiation attempts the network is normally fully aware of these, and network-based parameters should therefore correspond well to the same parameters as measured by an endpoint test tool (assuming that MT initiation attempts are known and controlled by the endpoint test tool). As most initiation procedures require a successful two-way communication during the initiation phase, the accessibility parameters measured for MO and MT endpoint test calls should normally not differ too much, and thus the network-based parameters for the MT case can be seen as a good approximation of the total network state.
+- **Time parameters:** Reflects the time needed to initiate a connection or a service. As these parameters are only defined for successful attempts the network can see the message flow, and can measure the time elapsed to initiate the connection or the service.
+
+The difference in parameter values as compared to the corresponding endpoint test measurements depend on where in the network the time measurements are done, but normally the time elapsed in the radio link and the processing time in the mobile are not included in the network-based parameters, making them more optimistic.
+
+If the excluded radio delay is stable or small compared to the total delay, the network-based measurements can still give a good picture on the state of the network.
+
+The estimated value of the excluded delay parts (for instance the radio delay) should be added to (or noted together with) the measured time parameter.
+
+### 12.2.3 Service retainability QoS parameters
+
+Retainability QoS parameters reflect the ability to retain, or keep a service up and running. Typical examples of retainability parameters are cut-off ratio and session failure ratio. Retainability parameters can be measured at the end-point but in general also inside the network. Measurements inside the network in general do not need any additional measurement data from the end-point.
+
+### 12.2.4 Service integrity QoS parameters
+
+Service integrity QoS parameters reflect the quality of a service that has been successfully set up and is in use. As the integrity parameters are only measured for successfully connected services, the network will always be aware of the ongoing service, and can measure its performance.
+
+Depending on the type of service used, different types of integrity parameters are calculated:
+
+- Media quality parameters
+- Response time parameters
+- Data rate parameters
+
+#### 12.2.4.1 Media quality parameters
+
+In an end-point test scenario, the media quality parameters can be measured in the mobile device by using objective quality algorithms, for instance [ITU-T P.862.1] for speech quality measurements. For the network-based quality measurements other methods are used:
+
+- Parameters related to the service quality can be measured at different nodes in the network and translated to a service quality parameter by using a parametric or bitstream quality model, like [ITU-T P.564] or similar model for video and multimedia service. The resulting quality parameter will reflect the state at the measurement nodes, and be an estimate of the end-user quality.
+- Parameters related to the service quality can be measured in the mobile, and reported back to POR in a node in the network (see Appendix XI). The calculation node combines the reported parameters with other relevant information collected from the network about the ongoing service, and calculates the service quality parameter. The resulting quality parameter will be closely correlated to the end-user experience.
+
+#### 12.2.4.2 Response time parameters
+
+Some services are characterized by real time events or real time interaction between the users, and the response time needs to be short enough to provide a good service experience. A typical service example is PoC which is dependent on short delays between user input and system acknowledgement.
+
+The time elapsed between certain protocol events can normally be measured in different network nodes. Some parts of the response time (typically the radio part) will not be included in the network-based measurements, so the resulting response time parameters will only be an estimate of the end-user experience.
+
+However, if the radio delay is stable or small compared to the total delay, the network-based measurements can still give relevant information about the end-user experience. It might also in some cases be possible to make a separate measurement or estimate of the radio delay.
+
+Any estimated value of the excluded delay parts (for instance the radio delay) should be added to (or noted together with) the measured time parameters.
+
+#### 12.2.4.3 Data rate parameters
+
+Data rate parameters, for services such as FTP, web browsing and e-mail, can be measured at different nodes in the network. Normally these services are using acknowledged radio bearers, and the network-based data rate measurements should thus be closely correlated to the corresponding endpoint test measurements.
+
+### 12.3 Comparing network and end-point test measurements
+
+Except for the differences due to different PCO for end-point tests tools and network measurements, there are other factors that affect the result. For instance the user behaviour might not be the same, as in the following example:
+
+*There is a radio network coverage problem for a series of tunnels in a newly built motorway area. Mobile users frequently get their calls dropped when they pass this area of the motorway. Initially when the motorway opened, the call drop rate measured in the network was high, since most of the calls were dropped. However, after some time the mobile users frequently passing this problem area will learn that the call will drop, and start to avoid making calls when passing that point. The drop rate in the network will go down but the problem has actually not been solved.*
+
+*If the same area is also monitored by, e.g., automatic end-point test equipment the drop rate will not go down, since the end-point test tool calling frequency is not reduced due to the coverage problem.*
+
+The network measurement results and the end-point test tool measurements are both correct, yet they might have a very different drop rate. This illustrates that even if the same thing is measured in both cases, the results should not always be expected to be the same.
+
+Another important difference between end-point test tools and network measurements is the possibility to get a good geographical position for the end-point test tools data, while network data typically is limited to cell level resolution. Thus even if network data in many cases can measure the same parameters, specific problems discovered in a cell might still need further support from endpoint testing to pinpoint where in the cell the problem occurs.
+
+## Appendix I
+
+### Examples for measuring trigger points
+
+(This appendix does not form an integral part of this Recommendation.)
+
+- SMS service:
+ - Layer 3 messages:
+ - Start SMS service attempt: generating random access (chan\_request SDCCH) at mobile equipment.
+ - Successful SMS service attempt receiving cp\_data (rp\_ack) at mobile equipment.
+ - Receiving SMS on mobile equipment 2: receiving cp\_data (rp\_ack) at mobile equipment.
+
+Additional examples are for further study.
+
+## Appendix II
+
+### Streaming explanations
+
+(This appendix does not form an integral part of this Recommendation.)
+
+#### Real time protocol (RTP)
+
+The real time protocol (RTP) is used for the transmission of real time data, e.g., audio, video, simulation data over multicast or unicast network services. No QoS functionality is implemented.
+
+RTP is designed to be independent from the underlying transport and network layers. For a complete description refer to [b-IETF RFC 3550].
+
+#### Real time control protocol (RTCP)
+
+The real time control protocol (RTCP) is known as the control protocol for the RTP. It allows the monitoring of the data delivery and provides a minimal control and identification functionality. RTCP is designed to be independent from the underlying transport and network layers.
+
+For a complete description of the RTCP refer to [b-IETF RFC 3550].
+
+#### Real time streaming protocol (RTSP)
+
+The real time streaming protocol (RTSP) is used for the overall control of the streaming session.
+
+For a complete description of the RTSP refer to [IETF RFC 2326].
+
+Most important methods of RTSP:
+
+- DESCRIBE: The DESCRIBE method retrieves the description of a presentation or media object identified by the request URL from a server. It may use the Accept header to specify the description formats that the client understands. The server responds with a *description* of the requested resource. The DESCRIBE reply-response pair constitutes the media initialization phase of RTSP [IETF RFC 2326].
+- SETUP: Causes the server to allocate resources for a stream and start an RTSP session [IETF RFC 2326].
+- PLAY: Play is sent from the client to the server and informs the server to start the transmission of data as specified by the SETUP method [IETF RFC 2326].
+- PAUSE: Sent from client to server. Temporarily halts the stream transmission without freeing server resources. These resources can only be freed after a specified time [IETF RFC 2326].
+- RECORD: This method initiates recording a range of media data according to the presentation description [IETF RFC 2326].
+- TEARDOWN: Frees resources associated with the stream. The RTSP session ceases to exist on the server [IETF RFC 2326].
+
+The following syntax for the hyperlink is used in order to access streaming content on the server:
+
+*protocol://address:port/path/file*
+
+| | |
+|----------|-------------------------------------------------------|
+| Protocol | Used protocol. E.g., rtsp:// |
+| Address | Address of the used streaming server |
+| Port | Port used by the server for answering request |
+| Path | Path to the file to be streamed |
+| File | The streaming file to be reproduced and its extension |
+
+## Appendix III
+
+### Push to talk over cellular information
+
+(This appendix does not form an integral part of this Recommendation.)
+
+Figures III.1 to III.4 visualize signal flows of typical PoC sessions. The figures include the signal flows on the transport layer as well as some restricted information on the application layer. To keep the flows concise, some signals are not shown. So, it is possible to obtain signal flows universally valid for different kinds of PoC sessions. Figures III.1 to III.4 also show particularities using unconfirmed indication with media buffering as well as differences between pre-established and on-demand PoC sessions.
+
+
+
+The diagram illustrates the signal flow for an on-demand PoC session with manual-answer. It involves three main entities: Terminal A, PoC network, and Terminal B. The sequence of messages is as follows:
+
+- Registration Phase:**
+ - Terminal A sends a **SIP REGISTER** to the PoC network. The network responds with **SIP 401 Unauthorized**, then **SIP REGISTER**, and finally **SIP 200 OK**.
+ - Terminal A then sends a **SIP PUBLISH** to the network, which responds with **SIP 200 OK**.
+ - Terminal B performs a similar registration: **SIP REGISTER** → **SIP 401 Unauthorized** → **SIP REGISTER** → **SIP 200 OK**, followed by **SIP PUBLISH** → **SIP 200 OK**.
+- Call Setup Phase:**
+ - Terminal A sends a **Talk Burst Request (Pushing PoC button)** to the network, which forwards it to Terminal B via **SIP INVITE**.
+ - Terminal B responds with **SIP 180 Ringing** → **SIP 200 OK** to the network, which then forwards it to Terminal A.
+ - Terminal A sends a **SIP ACK** to the network, which forwards it to Terminal B via **TBCP: Talk Burst Taken**.
+ - Terminal A receives a **Talk Burst Granted indication** from the network via **TBCP: Talk Burst Granted**.
+- Media Flow Phase:**
+ - Upon receiving the granted indication, Terminal A starts speech, indicated by **Start of speech**.
+ - Media is transmitted via **RTP: Media stream** from Terminal A to Terminal B through the network.
+ - When speech ends, Terminal A sends **RTP: Last Packet** and **TBCP: Talk Burst Idle** to the network, which then forwards them to Terminal B.
+ - Terminal B receives **End of speech** indication.
+- Second Call Setup (Reverse):**
+ - Terminal B then sends its own **Talk Burst Request (Pushing PoC button)** to the network via **TBCP: Talk Burst Request**.
+ - The network responds with **TBCP: Talk Burst Granted** to Terminal B.
+ - Terminal B starts speech (**Start of speech**).
+ - Media is transmitted via **RTP: Media stream** from Terminal B to Terminal A through the network.
+ - When speech ends, Terminal B sends **RTP: Last Packet** and **TBCP: Talk Burst Idle** to the network, which forwards them to Terminal A.
+ - Terminal A receives **End of speech** indication.
+- Call Termination Phase:**
+ - Terminal A sends a **SIP BYE** to the network, which responds with **SIP 200 OK**.
+ - Terminal A then sends another **SIP REGISTER** to the network, which responds with **SIP 200 OK**.
+ - Terminal B performs a similar termination: **SIP BYE** → **SIP 200 OK**, followed by **SIP REGISTER** → **SIP 200 OK**.
+
+Vertical color bars on the lifelines indicate active periods: yellow for registration, orange for publish, green for ringing/granted, blue for talk burst, and pink for bye.
+
+Sequence diagram of an on-demand PoC session with manual-answer between Terminal A, PoC network, and Terminal B.
+
+E.804(14)\_FIII.1
+
+Figure III.1 – On-demand PoC session with manual-answer
+
+
+
+The sequence diagram illustrates the interaction between Terminal A, the PoC network, and Terminal B for an unconfirmed on-demand ad hoc PoC group session with automatic-answer. The sequence of messages is as follows:
+
+- Terminal A** sends a **SIP REGISTER** to the **PoC network**. The network responds with **SIP 401 Unauthorized**, **SIP REGISTER**, **SIP 200 OK**, **SIP PUBLISH**, and **SIP 200 OK**.
+- Terminal B** sends a **SIP REGISTER** to the **PoC network**. The network responds with **SIP 401 Unauthorized**, **SIP REGISTER**, **SIP 200 OK**, **SIP PUBLISH**, and **SIP 200 OK**.
+- Terminal A** sends a **Talk Burst Request (Pushing PoC button)** to the **PoC network**, which forwards it to **Terminal B** via **SIP INVITE**.
+- Terminal A** receives **SIP 200 OK** and **SIP ACK** from the network. The network then sends a **TBCP: Talk Burst Granted** indication to **Terminal A**.
+- Terminal A** starts speech, and the network forwards the **RTP: Media stream** to **Terminal B**.
+- Terminal B** receives **SIP 200 OK** and **TBCP: Talk Burst Taken** from the network, accepting the incoming PoC session.
+- Terminal B** starts speech, and the network forwards the **RTP: Media stream** to **Terminal A**.
+- Terminal B** ends speech, and the network forwards the **RTP: Last Packet** to **Terminal A**.
+- Terminal B** sends a **TBCP: Talk Burst Request (Pushing PoC button)** to the **PoC network**, which forwards it to **Terminal A** via **TBCP: Talk Burst Granted**.
+- Terminal B** starts speech, and the network forwards the **RTP: Media stream** to **Terminal A**.
+- Terminal A** ends speech, and the network forwards the **RTP: Last Packet** to **Terminal B**.
+- Terminal A** sends a **TBCP: Talk Burst Idle** message to the **PoC network**, which forwards it to **Terminal B**.
+- Terminal A** sends a **SIP BYE** to the **PoC network**, which responds with **SIP 200 OK**.
+- Terminal A** sends a **SIP REGISTER** to the **PoC network**, which responds with **SIP 200 OK**.
+
+Sequence diagram showing the interaction between Terminal A, PoC network, and Terminal B for an unconfirmed on-demand ad hoc PoC group session with automatic-answer. The diagram illustrates the SIP and TBCP messages exchanged during registration, talk burst request/grant, and session termination.
+
+E.804(14)\_FIII.2
+
+NOTE – The PoC server supports media buffering and send the Talk Burst confirm message after receiving automatic-answer message.
+
+**Figure III.2 – Unconfirmed on-demand ad hoc PoC group session with automatic-answer**
+
+
+
+The sequence diagram illustrates the interaction between Terminal A, the PoC network, and Terminal B for a confirmed pre-established session with manual-answer. The process is as follows:
+
+- Registration:**
+ - Terminal A sends a SIP REGISTER to the PoC network. The network responds with SIP 401 Unauthorized, then Terminal A re-registers with SIP REGISTER, receiving a SIP 200 OK.
+ - Terminal A sends a SIP PUBLISH to the PoC network, receiving a SIP 200 OK.
+ - Terminal B sends a SIP REGISTER to the PoC network. The network responds with SIP 401 Unauthorized, then Terminal B re-registers with SIP REGISTER, receiving a SIP 200 OK.
+ - Terminal B sends a SIP PUBLISH to the PoC network, receiving a SIP 200 OK.
+- Session Establishment (Terminal A to Terminal B):**
+ - Terminal A sends a Talk Burst Request (Pushing PoC button) to the PoC network.
+ - The PoC network sends a SIP INVITE to Terminal B.
+ - Terminal B responds with SIP 200 OK to the PoC network.
+ - The PoC network sends a SIP ACK to Terminal A.
+ - Terminal A receives a Talk Burst Granted indication from the PoC network.
+ - Terminal A sends a TBCP: Talk Burst Granted to the PoC network.
+ - Terminal A starts speech, and the PoC network begins an RTP: Media stream to Terminal B.
+ - Terminal B receives the SIP 200 OK and TBCP: Talk Burst Taken from the PoC network.
+ - Terminal B accepts the incoming PoC session.
+ - The PoC network sends an RTP: Media stream to Terminal B.
+ - Terminal B sends an RTP: Last Packet to the PoC network.
+ - Terminal B sends a TBCP: Talk Burst Idle to the PoC network.
+- Session Establishment (Terminal B to Terminal A):**
+ - Terminal B sends a Talk Burst Request (Pushing PoC button) to the PoC network.
+ - The PoC network sends a TBCP: Talk Burst Request to Terminal A.
+ - Terminal A responds with a TBCP: Talk Burst Granted to the PoC network.
+ - The PoC network sends an RTP: Media stream to Terminal A.
+ - Terminal A starts speech.
+ - Terminal A sends an RTP: Last Packet to the PoC network.
+ - Terminal A sends a TBCP: Talk Burst Idle to the PoC network.
+ - Terminal A receives an End of speech indication from the PoC network.
+- Session Termination:**
+ - Terminal A sends a SIP BYE to the PoC network, which is responded to with a SIP 200 OK.
+ - Terminal B sends a SIP BYE to the PoC network, which is responded to with a SIP 200 OK.
+ - Both terminals re-register with the PoC network using SIP REGISTER and receive SIP 200 OK responses.
+
+Sequence diagram showing a confirmed pre-established session with manual-answer between Terminal A, PoC network, and Terminal B. The diagram illustrates the SIP and TBCP signaling and RTP media streams for a Push-to-Talk conversation.
+
+E.804(14)\_FIII.3
+
+Figure III.3 – Confirmed pre-established session with manual-answer
+
+
+
+The sequence diagram illustrates the following signal flow:
+
+- Terminal A** sends a **SIP REGISTER** to the **PoC network**, which responds with **SIP 401 Unauthorized** and then **SIP 200 OK**.
+- Terminal A** sends a **SIP PUBLISH** to the **PoC network**, which responds with **SIP 200 OK**.
+- Terminal B** sends a **SIP REGISTER** to the **PoC network**, which responds with **SIP 401 Unauthorized** and then **SIP 200 OK**.
+- Terminal B** sends a **SIP PUBLISH** to the **PoC network**, which responds with **SIP 200 OK**.
+- Terminal A** initiates a **Pre-established session initiation** with a **SIP INVITE** to the **PoC network**, which responds with **SIP 100 Trying**, **SIP 200 OK**, and **SIP ACK**.
+- Terminal A** sends a **Talk Burst Request (Pushing PoC button)** to the **PoC network**, which responds with **SIP REFER** and **SIP 202 Accepted**.
+- Terminal A** receives a **TBCP: Connect** from the **PoC network**, which responds with **TBCP: Acknowledged**.
+- Terminal A** receives a **Talk Burst Granted indication** from the **PoC network**, which responds with **TBCP: Talk Burst Granted**.
+- Terminal A** starts speech, sending an **RTP: Media stream** to the **PoC network**, which forwards it to **Terminal B**.
+- Terminal A** sends an **RTP: Last Packet** to the **PoC network**, which forwards it to **Terminal B**.
+- Terminal A** sends a **TBCP: Talk Burst Idle** to the **PoC network**, which responds with **TBCP: Talk Burst Taken**.
+- Terminal A** receives an **End of speech** indication from the **PoC network**, which responds with **TBCP: Talk Burst Idle**.
+- Terminal A** sends a **SIP BYE** to the **PoC network**, which responds with **SIP 200 OK**.
+- Terminal A** sends a **SIP REGISTER** to the **PoC network**, which responds with **SIP 200 OK**.
+- Terminal B** receives a **SIP INVITE** from the **PoC network**, which responds with **SIP 100 Trying**, **SIP 200 OK**, and **SIP ACK**.
+- Terminal B** receives a **TBCP: Connect** from the **PoC network**, which responds with **TBCP: Acknowledged**.
+- Terminal B** receives a **TBCP: Talk Burst Taken** from the **PoC network**, which responds with **TBCP: Talk Burst Granted**.
+- Terminal B** starts speech, sending an **RTP: Media stream** to the **PoC network**, which forwards it to **Terminal A**.
+- Terminal B** sends an **RTP: Last Packet** to the **PoC network**, which forwards it to **Terminal A**.
+- Terminal B** sends a **TBCP: Talk Burst Idle** to the **PoC network**, which responds with **TBCP: Talk Burst Taken**.
+- Terminal B** receives a **Talk Burst Request (Pushing PoC button)** from the **PoC network**, which responds with **TBCP: Talk Burst Granted**.
+- Terminal B** starts speech, sending an **RTP: Media stream** to the **PoC network**, which forwards it to **Terminal A**.
+- Terminal B** sends an **RTP: Last Packet** to the **PoC network**, which forwards it to **Terminal A**.
+- Terminal B** sends a **TBCP: Talk Burst Idle** to the **PoC network**, which responds with **TBCP: Talk Burst Taken**.
+- Terminal B** receives an **End of speech** indication from the **PoC network**, which responds with **TBCP: Talk Burst Idle**.
+- Terminal B** sends a **SIP BYE** to the **PoC network**, which responds with **SIP 200 OK**.
+- Terminal B** sends a **SIP REGISTER** to the **PoC network**, which responds with **SIP 200 OK**.
+
+Sequence diagram showing the signal flow for an unconfirmed pre-established session with automatic-answer between Terminal A, PoC network, and Terminal B. The diagram illustrates the interaction for session initiation, talk burst requests, and session termination.
+
+E.804(14)\_FIII.4
+
+**Figure III.4 – Unconfirmed pre-established session with automatic-answer**
+
+### III.1 Signal grouping
+
+This clause defines groups of signals which will in the following be referred to as building blocks of PoC signal flows, or just building blocks. These building blocks are derived from [b-ETSI TS 123 246], [OMA-1] and [OMA-2] representing only parts of a complete signal flow as seen in Figures III.1 to III.4. Here, different building blocks of the same kind correspond to the same QoS group. The aim of the definition of such building blocks is to give detailed information on the different signal flows.
+
+Remark:
+
+- In the QoS parameter defining clause of this Recommendation, most signal flows shown are less detailed. The reason for this is that these flows are only used to visualize the relevant trigger points of the corresponding QoS parameter with respect to their occurrence over time.
+
+The relationship between building blocks and QoS groups is depicted in the following table. In contrast to the signal flows given to illustrate QoS parameter definition, only flows leading to a positive result are given. The only exception from this is the signal flow for a queued talk burst request which is added for sufficiency.
+
+A distinction has been made between on-demand and pre-established PoC sessions since here different building blocks are needed. Crosses are indicating the blocks needed for the corresponding QoS group. For simplicity some crosses are greyed (i.e., shown with lighter colour). These crosses indicate that a choice between confirmed and unconfirmed indication has to be made.
+
+Further parameters for the "Session SETUP" are the following:
+
+- Session SETUP alternative 1: confirmed with auto-answered on terminating side.
+- Session SETUP alternative 2: confirmed with manual answered on terminating side.
+- Session SETUP alternative 3: unconfirmed with auto-answered on terminating side.
+- Session SETUP alternative 4: unconfirmed with manual answered on terminating side.
+
+Remarks:
+
+- Only the QoS groups relevant to the building blocks are shown in Table III.1.
+- Building blocks not related to any QoS group are omitted in Table III.1.
+- Building blocks can be identified by their number as specified in Table III.1.
+
+Table III.1 – Assignment of PoC session parts to building blocks
+
+| Building blocks (below) and QoS groups (right hand side) | | REG | PUB | REG long | On-demand | | | | Pre-established | | | | | | DeREG |
+|----------------------------------------------------------|------------------------------------------------------------|-----|-----|----------|-----------|---------------|-----|-------|-----------------|------|---------------|-----|-------|---|-------|
+| | | | | | INIT | Session SETUP | PtS | LEAVE | NEG | INIT | Session SETUP | PtS | LEAVE | | |
+| 1 | PoC service registration | X | X | x | x | x | x | x | | x | x | x | x | | x |
+| 2 | PoC publish | | x | x | x | x | x | x | | x | x | x | x | | |
+| 3a | PoC on-demand session Initiation, confirmed | | | | x | x | x | | | | | | | | |
+| 3b | PoC on-demand session initiation, unconfirmed | | | | x | x | x | | | | | | | | |
+| 3c | PoC pre-established session media parameters negotiation | | | | | | | | x | x | x | x | | | |
+| 3d | PoC pre-established session initiation, confirmed | | | | | | | | | x | x | x | | | |
+| 3e | PoC pre-established session initiation, unconfirmed | | | | | | | | | x | x | x | | | |
+| 4a | PoC on-demand session initiation, user B auto-answer | | | | x | x | x | | | | | | | | |
+| 4b | PoC on-demand session initiation, user B manual-answer | | | | x | x | x | | | | | | | | |
+| 4c | PoC Pre-established session initiation, user B auto-answer | | | | | | | | | x | x | x | | | |
+| 4d | PoC pre-established session initiation, user B | | | | | | | | | x | x | x | | | |
+| 5a | Media stream from user A to PoC server | | | | | | | | | | | x | | | |
+| 5b | Media stream from PoC server to user B, without buffer | | | | | | | | | | | x | | | |
+| 5c | Media stream from user B to user A, without buffer | | | | | | | | | | | x | | | |
+| 6b | Talk burst request | | | | | | | | | | | x | | | |
+| 6c | Queued talk burst request | | | | | | | | | | | x | | | |
+| 7a | Leaving PoC session (on-demand) | | | | | | | | | | | | x | | |
+| 7b | Leaving PoC session (pre-established) | | | | | | | | | | | | | x | |
+| 8 | Deregistration | | | | | | | | | | | | | | x |
+
+### III.2 PoC service registration
+
+Figure III.5 shows the PoC service registration signal flow.
+
+
+
+```
+
+sequenceDiagram
+ participant User
+ participant Terminal A
+ participant PoC servers
+ Note left of Terminal A: User A PoC service registration
+ Terminal A->>PoC servers: SIP REGISTER
+ PoC servers-->>Terminal A: SIP 401 Unauthorized
+ Terminal A->>PoC servers: SIP REGISTER
+ PoC servers-->>Terminal A: SIP 200 OK
+
+```
+
+E.804(14)\_FIII.5
+
+Sequence diagram for PoC service registration. Terminal A sends a SIP REGISTER to PoC servers, which responds with 401 Unauthorized. Terminal A resends the REGISTER, and the server responds with 200 OK.
+
+**Figure III.5 – PoC service registration signal flow**
+
+### III.3 PoC publish
+
+Figure III.6 shows the publish signal flow.
+
+
+
+```
+
+sequenceDiagram
+ participant Terminal A
+ participant PoC servers
+ Terminal A->>PoC servers: SIP PUBLISH
+ PoC servers-->>Terminal A: SIP 200 OK
+
+```
+
+E.804(14)\_FIII.6
+
+Sequence diagram for PoC publish. Terminal A sends a SIP PUBLISH to PoC servers, which responds with 200 OK.
+
+**Figure III.6 – PoC publish signal flow**
+
+### III.4 PoC session initiation, originating part
+
+Figure III.7 shows the PoC on-demand session initiation signal flow (confirmed, originating part).
+
+
+
+```
+
+sequenceDiagram
+ participant User
+ participant Terminal A
+ participant PoC servers
+ Note left of Terminal A: Talk Burst Request (Pushing PoC button)
+ Terminal A->>PoC servers: SIP INVITE
+ PoC servers-->>Terminal A: SIP 180 Ringing
+ PoC servers-->>Terminal A: SIP 200 OK
+ Terminal A->>PoC servers: SIP ACK
+ PoC servers-->>Terminal A: TBCP: Talk Burst Granted
+ Note left of Terminal A: Talk Burst Granted indication
+
+```
+
+E.804(14)\_FIII.7
+
+Sequence diagram for PoC on-demand session initiation (confirmed). Terminal A sends a SIP INVITE to PoC servers. The server responds with 180 Ringing, then 200 OK. Terminal A sends a SIP ACK. Finally, a TBCP: Talk Burst Granted message is sent from the server to the user.
+
+**Figure III.7 – PoC on-demand session initiation signal flow (confirmed, originating part)**
+
+Figure III.8 shows the PoC on-demand session initiation (unconfirmed, originating part).
+
+
+
+```
+
+sequenceDiagram
+ participant User
+ participant Terminal A
+ participant PoC servers
+ Note left of Terminal A: Talk Burst Request (Pushing PoC button)
+ Terminal A->>PoC servers: SIP INVITE
+ PoC servers-->>Terminal A: SIP 200 OK (unconfirmed)
+ Terminal A->>PoC servers: SIP ACK
+ PoC servers-->>Terminal A: TBCP: Talk Burst Granted
+ Note left of Terminal A: Talk Burst Granted indication
+
+```
+
+E.804(14)\_FIII.8
+
+Sequence diagram for PoC on-demand session initiation (unconfirmed). Terminal A sends a SIP INVITE to PoC servers. The server responds with 200 OK (unconfirmed). Terminal A sends a SIP ACK. Finally, a TBCP: Talk Burst Granted message is sent from the server to the user.
+
+**Figure III.8 – PoC on-demand session initiation signal flow (unconfirmed, originating part)**
+
+Figure III.9 shows the PoC pre-established session media parameters negotiation (originating part).
+
+
+
+```
+
+sequenceDiagram
+ participant Terminal A
+ participant PoC servers
+ Note left of Terminal A: Session establishment
+ Terminal A->>PoC servers: SIP INVITE
+ PoC servers-->>Terminal A: SIP 100 Trying
+ PoC servers-->>Terminal A: SIP 200 OK
+ Terminal A->>PoC servers: SIP ACK
+
+```
+
+E.804(14)\_FIII.9
+
+Sequence diagram for PoC pre-established session media parameters negotiation (originating part). It shows Terminal A sending a SIP INVITE to PoC servers, which respond with 100 Trying, 200 OK, and ACK. A dashed line on the left indicates the start of session establishment.
+
+**Figure III.9 – PoC pre-established session media parameters negotiation signal flow (originating part)**
+
+Figure III.10 shows the PoC pre-established session initiation signal flow (confirmed, originating part)
+
+
+
+```
+
+sequenceDiagram
+ participant Terminal A
+ participant PoC servers
+ Note over Terminal A, PoC servers: Pre-established session
+ Note left of Terminal A: Talk Burst Request (Pushing PoC button)
+ Terminal A->>PoC servers: SIP REFER
+ PoC servers-->>Terminal A: SIP 202 Accepted
+ PoC servers-->>Terminal A: SIP NOTIFY
+ PoC servers-->>Terminal A: SIP 200 OK
+ Note right of PoC servers: Inviting user
+ Note right of PoC servers: Ringing response received
+ PoC servers-->>Terminal A: SIP NOTIFY
+ PoC servers-->>Terminal A: SIP 200 OK
+ Note right of PoC servers: Invitation has been accepted
+ PoC servers-->>Terminal A: SIP NOTIFY
+ PoC servers-->>Terminal A: SIP 200 OK
+ PoC servers-->>Terminal A: SIP NOTIFY
+ PoC servers-->>Terminal A: SIP 200 OK
+ Terminal A->>PoC servers: TBCP: Connect
+ PoC servers-->>Terminal A: TBCP: Acknowledged
+ Note left of Terminal A: Talk Burst Granted indication
+ PoC servers-->>Terminal A: TBCP: Talk Burst Granted
+
+```
+
+E.804(14)\_FIII.10
+
+Sequence diagram for PoC pre-established session initiation signal flow (confirmed, originating part). It starts with a 'Pre-established session' oval. Terminal A sends a SIP REFER (Talk Burst Request) to PoC servers. The servers respond with 202 Accepted, NOTIFY, and 200 OK. The server then invites the user, receives a ringing response, and sends further NOTIFY messages. Finally, TBCP messages (Connect, Acknowledged, Talk Burst Granted) are exchanged, ending with a 'Talk Burst Granted indication' to Terminal A.
+
+**Figure III.10 – PoC pre-established session initiation signal flow (confirmed, originating part)**
+
+Figure III.11 shows PoC pre-established session initiation (unconfirmed, originating part).
+
+
+
+This sequence diagram illustrates the signal flow for initiating a PoC pre-established session from Terminal A to PoC servers. The process begins with a 'Talk Burst Request (Pushing PoC button)' from Terminal A. Terminal A then sends a 'SIP REFER' to the PoC servers, which respond with 'SIP 202 Accepted', 'SIP NOTIFY', and 'SIP 200 OK'. The PoC servers then send another 'SIP NOTIFY' and 'SIP 200 OK', with a note indicating 'Invitation has been accepted'. Finally, Terminal A sends 'TBCP: Connect', the PoC servers respond with 'TBCP: Acknowledged', and Terminal A receives 'TBCP: Talk Burst Granted', which leads to a 'Talk Burst Granted indication' back to the user. The diagram is labeled E.804(14)\_FIII.11.
+
+Sequence diagram for PoC pre-established session initiation signal flow (unconfirmed, originating part).
+
+**Figure III.11 – PoC pre-established session initiation signal flow (unconfirmed, originating part)**
+
+### III.5 PoC session initiation, terminating part
+
+Figure III.12 shows the PoC on-demand session signal flow (automatic answer terminating part).
+
+
+
+This sequence diagram shows the signal flow for an on-demand PoC session with automatic answer at the terminating part. PoC servers send a 'SIP INVITE' to Terminal B, which automatically accepts the incoming PoC session. Terminal B responds with 'SIP 200 OK' and 'TBCP: Talk Burst Granted'. The diagram is labeled E.804(14)\_FIII.12.
+
+Sequence diagram for PoC on-demand session signal flow (automatic answer, terminating part).
+
+**Figure III.12 – PoC on-demand session signal flow (automatic answer, terminating part)**
+
+Figure III.13 shows the PoC On-demand session signal flow (manual answer, terminating part).
+
+
+
+This sequence diagram illustrates the signal flow for an on-demand PoC session with manual answer at the terminating part. PoC servers send a 'SIP INVITE' to Terminal B. Terminal B responds with 'SIP 180 Ringing', then 'SIP 200 OK' after the user manually accepts the incoming PoC session, and finally 'TBCP: Talk Burst Taken'. The diagram is labeled E.804(14)\_FIII.13.
+
+Sequence diagram for PoC on-demand session signal flow (manual answer, terminating part).
+
+**Figure III.13 – PoC on-demand session signal flow (manual answer, terminating part)**
+
+Figure III.14 shows PoC a pre-established session signal flow (automatic answer, terminating part).
+
+
+
+This sequence diagram illustrates the signal flow for the automatic answer, terminating part of a PoC pre-established session. It features two lifelines: 'PoC servers' on the left and 'Terminal B' on the right. A horizontal oval at the top indicates a 'Pre-established session'. The sequence of messages is as follows:
+
+
+- An arrow labeled 'TBCP: Connect' points from the PoC servers to Terminal B.
+- A return arrow labeled 'TBCP: Talk Burst Ack' points from Terminal B back to the PoC servers.
+- A final arrow labeled 'TBCP: Talk Burst Taken' points from the PoC servers to Terminal B.
+
+ The diagram is labeled 'E.804(14)\_FIII.14' at the bottom right.
+
+Sequence diagram for PoC pre-established session signal flow (automatic answer, terminating part).
+
+**Figure III.14 – PoC pre-established session signal flow (automatic answer, terminating part)**
+
+Figure III.15 shows the PoC pre-established session signal flow (manual answer, terminating part).
+
+
+
+This sequence diagram illustrates the signal flow for the manual answer, terminating part of a PoC pre-established session. It features two lifelines: 'PoC servers' on the left and 'Terminal B' on the right. A horizontal oval at the top indicates a 'Pre-established session'. The sequence of messages is as follows:
+
+
+- An arrow labeled 'SIP INVITE' points from the PoC servers to Terminal B.
+- A return arrow labeled 'SIP 180 Ringing' points from Terminal B back to the PoC servers.
+- An arrow labeled 'SIP 200 OK' points from Terminal B to the PoC servers. A dashed line from the right points to this message with the text 'User B accepts incoming PoC session manually'.
+- A final arrow labeled 'TBCP: Talk Burst Taken' points from the PoC servers to Terminal B.
+
+ The diagram is labeled 'E.804(14)\_FIII.15' at the bottom right.
+
+Sequence diagram for PoC pre-established session signal flow (manual answer, terminating part).
+
+**Figure III.15 – PoC pre-established session signal flow (manual answer, terminating part)**
+
+### III.6 Media Streaming
+
+Figure III.16 shows the signal flow for the first media stream from user A to PoC server.
+
+
+
+This sequence diagram illustrates the signal flow for the first media stream from user A to the PoC server. It features two lifelines: 'Terminal A' on the left and 'PoC servers' on the right. The sequence of messages is as follows:
+
+
+- A dashed line labeled 'Start of speech' points to the beginning of the first message on the Terminal A lifeline.
+- An arrow labeled 'RTP: Media stream' points from Terminal A to the PoC servers.
+- A return arrow labeled 'RTP: Last Packet' points from the PoC servers back to Terminal A.
+- A final return arrow labeled 'TBCP: Talk Burst Idle' points from the PoC servers back to Terminal A.
+
+ The diagram is labeled 'E.804(14)\_FIII.16' at the bottom right.
+
+Sequence diagram for signal flow for first media stream from user A to PoC server.
+
+**Figure III.16 – Signal flow for first media stream from user A to PoC server**
+
+Figure III.17 shows the signal flow for the first media stream from PoC server to user B (without media buffering).
+
+
+
+```
+
+sequenceDiagram
+ participant PoC servers
+ participant Terminal B
+ Note right of Terminal B: End of speech
+ PoC servers->>Terminal B: RTP: Media stream
+ PoC servers->>Terminal B: RTP: Last Packet
+ PoC servers->>Terminal B: TBCP: Talk Burst Idle
+
+```
+
+Sequence diagram showing signal flow for the first media stream from PoC servers to Terminal B. The diagram shows three messages: RTP: Media stream, RTP: Last Packet, and TBCP: Talk Burst Idle. The 'End of speech' is indicated by a dashed line after the last packet.
+
+E.804(14)\_FIII.17
+
+**Figure III.17 – Signal flow for first media stream from PoC server to user B (no media buffering)**
+
+Figure III.18 shows the last media stream from user B to user A via PoC network (without media buffering), including Talk Burst Request of user B.
+
+
+
+```
+
+sequenceDiagram
+ participant Terminal A
+ participant PoC servers
+ participant Terminal B
+ Note right of Terminal B: Talk Burst Request (Pushing PoC button)
+ Terminal B->>PoC servers: TBCP: Talk Burst Request
+ PoC servers->>Terminal B: TBCP: Talk Burst Granted
+ Note right of Terminal B: Start of speech
+ Terminal B->>PoC servers: RTP: Media stream
+ PoC servers->>Terminal A: RTP: Media stream
+ Terminal B->>PoC servers: RTP: Last Packet
+ PoC servers->>Terminal A: RTP: Last Packet
+ Note left of Terminal A: End of speech
+ Terminal B->>PoC servers: TBCP: Talk Burst Idle
+ PoC servers->>Terminal A: TBCP: Talk Burst Idle
+
+```
+
+Sequence diagram showing signal flow for the last media stream from Terminal B to Terminal A via PoC servers. It includes TBCP: Talk Burst Request, TBCP: Talk Burst Granted, RTP: Media stream, RTP: Last Packet, and TBCP: Talk Burst Idle messages. 'End of speech' and 'Start of speech' are indicated by dashed lines.
+
+E.804(14)\_FIII.18
+
+**Figure III.18 – Signal flow for last media stream from user B to user A via PoC network (no media buffering)**
+
+### III.7 Talk burst request
+
+Figure III.19 shows the signal flow for implicit talk burst request (on-demand session initiation).
+
+
+
+```
+
+sequenceDiagram
+ participant Terminal A
+ participant PoC servers
+ Note left of Terminal A: Talk Burst Request (Pushing PoC button)
+ Terminal A->>PoC servers: SIP INVITE
+ PoC servers->>Terminal A: SIP 180 Ringing
+ Terminal A->>PoC servers: SIP 200 OK
+ PoC servers->>Terminal A: SIP ACK
+ Note left of Terminal A: Talk Burst Granted indication
+ Terminal A->>PoC servers: TBCP: Talk Burst Granted
+
+```
+
+Sequence diagram showing signal flow for an implicit talk burst request. It starts with a 'Talk Burst Request (Pushing PoC button)' from Terminal A to PoC servers, followed by SIP INVITE, SIP 180 Ringing, SIP 200 OK, and SIP ACK. A 'Talk Burst Granted indication' is then sent from Terminal A to PoC servers, and finally a 'TBCP: Talk Burst Granted' message is sent from PoC servers to Terminal A.
+
+E.804(14)\_FIII.19
+
+**Figure III.19 – Signal flow for implicit talk burst request (on-demand session initiation)**
+
+Figure III.20 shows the signal flow for explicit talk burst request.
+
+
+
+```
+
+sequenceDiagram
+ participant PoC servers
+ participant Terminal B
+ Note right of Terminal B: Talk Burst Request (Pushing PoC button)
+ Terminal B->>PoC servers: TBCP: Talk Burst Request
+ PoC servers->>Terminal B: TBCP: Talk Burst Granted
+
+```
+
+Sequence diagram showing signal flow for an explicit talk burst request. It shows a 'Talk Burst Request (Pushing PoC button)' from Terminal B to PoC servers, followed by 'TBCP: Talk Burst Request' and 'TBCP: Talk Burst Granted' messages between Terminal B and PoC servers.
+
+E.804(14)\_FIII.20
+
+**Figure III.20 – Signal flow for explicit talk burst request**
+
+Figure III.21 shows the signal flow for queued talk burst request.
+
+
+
+This sequence diagram illustrates the signal flow for a queued talk burst request between PoC servers and Terminal B. The interaction starts with an RTP: Media stream from PoC servers to Terminal B. Terminal B then sends a TBCP: Talk Burst Request (Pushing PoC button) to PoC servers. PoC servers respond with a TBCP: Talk Burst Queued (position). Terminal B continues to send RTP: Last Packet. PoC servers then send a TBCP: Talk Burst Confirm. Finally, Terminal B sends an End of speech indication, PoC servers send a Floor granted indication, and Terminal B sends a Start of speech indication. The diagram is labeled E.804(14)\_FIII.21.
+
+Sequence diagram for queued talk burst request between PoC servers and Terminal B.
+
+**Figure III.21 – Signal flow for queued talk burst request**
+
+### III.8 Leaving PoC session
+
+Figure III.22 shows the signal flow for leaving on-demand PoC session.
+
+
+
+This sequence diagram illustrates the signal flow for leaving an on-demand PoC session between Terminal A and PoC servers. Terminal A sends a SIP BYE message to PoC servers, which respond with a SIP 200 OK. The diagram is labeled E.804(14)\_FIII.22.
+
+Sequence diagram for leaving on-demand PoC session between Terminal A and PoC servers.
+
+**Figure III.22 – Signal flow for leaving on-demand PoC session**
+
+Figure III.23 shows the signal flow for leaving pre-established PoC session.
+
+
+
+This sequence diagram illustrates the signal flow for leaving a pre-established PoC session between Terminal A and PoC servers. Terminal A sends a SIP REFER message to PoC servers, which respond with a SIP 202 Accepted. PoC servers then send a SIP Notify to Terminal A, which responds with a SIP 200 OK. The diagram is labeled E.804(14)\_FIII.23.
+
+Sequence diagram for leaving pre-established PoC session between Terminal A and PoC servers.
+
+**Figure III.23 – Signal flow for leaving pre-established PoC session**
+
+### III.9 Deregistration
+
+Figure III.24 shows the PoC service deregistration signal flow.
+
+
+
+```
+sequenceDiagram
+ participant Terminal A
+ participant PoC servers
+ Note left of Terminal A: [Red Bar]
+ Terminal A->>PoC servers: SIP REGISTER
+ PoC servers-->>Terminal A: SIP 401 Unauthorized
+ Terminal A->>PoC servers: SIP REGISTER
+ PoC servers-->>Terminal A: SIP 200 OK
+```
+
+E.804(14)\_FIII.24
+
+Sequence diagram showing PoC service deregistration signal flow between Terminal A and PoC servers. The sequence consists of four messages: 1. Terminal A sends a SIP REGISTER to PoC servers. 2. PoC servers respond with a SIP 401 Unauthorized. 3. Terminal A sends another SIP REGISTER. 4. PoC servers respond with a SIP 200 OK. A red vertical bar is on Terminal A's lifeline between the first and second messages. A grey vertical bar is on the PoC servers' lifeline. The diagram is labeled E.804(14)\_FIII.24 at the bottom right.
+
+**Figure III.24 – PoC service deregistration signal flow**
+
+## Appendix IV
+
+### **QoS parameter export**
+
+(This appendix does not form an integral part of this Recommendation.)
+
+#### **IV.1 Overview**
+
+Goal of this file format is to have a common interface to be able to interchange measurement results between systems of different vendors. Data should be provided on the highest possible level of granularity to support the QoS parameters defined in this Recommendation by keeping the possibility to select by time, geographical position and operator information.
+
+The file format is XML. This Recommendation describes the use of XML bodies and tags.
+
+All relevant IDs for trigger points and QoS parameters can be found in clause 6.
+
+#### **IV.2 XML bodies**
+
+##### **IV.2.1 **
+
+This is the root of the XML document.
+
+
+
+```
+
+ erDiagram
+ Measurement ||--|| fileversion : ""
+ Measurement ||--|| System : ""
+ Measurement ||--|| Source : ""
+ Measurement ||--|| Configuration : ""
+ Measurement ||--|| Sequence : ""
+
+ fileversion {
+ string version
+ }
+
+ System {
+ string id
+ }
+
+ Source {
+ string ID
+ string FriendlyName
+ string DeviceID
+ }
+
+ Configuration {
+ string name
+ }
+
+ Configuration ||--|| Setting : ""
+ Setting {
+ string value
+ string name
+ }
+
+ Sequence {
+ }
+
+ Sequence ||--|| Value : ""
+ Value {
+ string value
+ string type
+ string start
+ string source
+ string name
+ string Type2
+ string Index
+ }
+
+ Sequence ||--|| Trigger : ""
+ Trigger {
+ string start
+ string source
+ string name
+ string id
+ }
+
+ Trigger ||--|| Value : ""
+ Value {
+ string value
+ string type
+ string start
+ string source
+ string name
+ string Type2
+ string Index
+ }
+
+ Sequence ||--|| qsi : ""
+ qsi {
+ }
+
+ qsi ||--|| Value : ""
+ Value {
+ string value
+ string start
+ string name
+ string id
+ string end
+ }
+
+ qsi ||--|| retrigger : ""
+ retrigger {
+ string id
+ }
+
+```
+
+A complex network diagram showing entities like Measurement, fileversion, System, Source, Configuration, Sequence, Value, Trigger, qsi, and retrigger with their attributes and relationships.
+
+## IV.2.2 Configuration
+
+### IV.2.2.1
+
+Version of the file definition.
+
+| | |
+|---------|--------------------------------------------------------------------|
+| Version | Version number of the document [b-ETSI TS 102 250-4]. E.g., 2.2.1. |
+|---------|--------------------------------------------------------------------|
+
+Example: