Scene2Wave

Scene2Wave is a synchronized multimodal wireless-channel dataset generated with CARLA and Sionna RT. Version 1.0.1 contains 100 formally validated samples spanning four CARLA Towns, five motion states, four radio profiles, multi-view cameras, Birdview, LiDAR, Radar, IMU, GNSS, poses, path-level CIR, and CSI.

The Scene2Wave Project Content rightsholder is Pengcheng Laboratory. Based on the current generation configuration, the dataset is known to use official CARLA assets and the official Sionna software stack; no separately sourced scene assets have been identified.

Authors

Mengfan Zheng, Liwen Jing, Tingting Yang, Li Sun, Yuxuan Shi, Ping Zhang, Leiyang Xu, and Jianjun Chen.

Dataset summary

Property	Value
Formal samples	100
Towns	Town01, Town02, Town03, Town15; 25 samples each
Motion states	static, 10, 20, 40, 60 km/h; 20 samples each
Profiles	core 40, multipath 20, scattering mild 20, scattering medium 20
Carrier frequency	3.7 GHz
Bandwidth	15.36 MHz
Subcarriers	512 at 30 kHz spacing
CSI sampling rate	2 kHz
Nominal sample duration	1 second
CARLA	0.9.16
Sionna	0.19.2

Propagation profiles within each motion state

Each motion state (static, 10kmh, 20kmh, 40kmh, or 60kmh) contains 20 samples: five distinct CARLA runs from each of the four Towns. Within every Town × motion-state bucket, those five runs are allocated as two core and one each of multipath, scattering_mild, and scattering_medium. Thus each motion state contains 8/4/4/4 samples respectively, and the complete release contains 40/20/20/20.

All four profiles enable LOS, specular reflection, diffraction, and edge diffraction. num_samples below is the Sionna ray-launching budget per source, not the number of dataset examples. max_depth is the maximum interaction depth used by the path solver.

Profile	Per Town × state	Per state	num_samples	max_depth	Scattering	Material profile / scale	scat_keep_prob
core	2	8	1,000,000	2	disabled	none / inactive	inactive
multipath	1	4	2,000,000	3	disabled	none / inactive	inactive
scattering_mild	1	4	2,000,000	3	enabled	urban_37ghz / 0.30	0.0002
scattering_medium	1	4	2,000,000	3	enabled	urban_37ghz / 0.60	0.0005

The profiles have the following intended meanings:

• core is the baseline propagation setting. Its shallower interaction depth and smaller ray budget emphasize dominant LOS, specular, and diffracted components while keeping the baseline representative and computationally bounded.
• multipath disables diffuse scattering like core, but doubles the ray budget and raises the interaction depth from 2 to 3. It is intended to retain more higher-order reflected/diffracted paths and represent a denser non-scattering multipath condition.
• scattering_mild uses the multipath solver budget and enables the urban_37ghz material-scattering model at scale 0.30. It introduces a moderate diffuse component for robustness and material-scattering sensitivity studies.
• scattering_medium raises the same material-scattering scale to 0.60 and increases scattering-path retention. It represents the more difficult diffuse-scattering condition included in the main training distribution.

material_scattering_scale is a relative multiplier applied to the configured urban material-scattering profile; it is not a percentage of total received power. Likewise, scat_keep_prob controls stochastic retention of candidate scattering paths for tractable simulation and is not the physical probability that a surface scatters.

These are independently generated main_training samples, not four RF reruns of identical CARLA geometry. Profile comparisons should therefore use stratified or aggregate analysis across Town and motion state; a difference between two individual samples must not be attributed solely to the profile.

Sensor suite and acquisition configuration

The CAV is a CARLA vehicle.tesla.model3. The roadside unit (RSU) is mounted 5 m above the sampled road surface. The following table records the formal release configuration; counts and rates are per sample unless stated otherwise.

Platform / modality	Configuration	Nominal output
CAV RGB cameras	4 views: front, rear, right, left; 1280 × 720; 110° horizontal FOV; camera height 2.4 m	20 Hz, 20 frames/view
CAV depth cameras	4 views co-located with the RGB cameras; 1280 × 720; 110° horizontal FOV	20 Hz, 20 frames/view
CAV LiDAR	128 channels; 120 m range; 360° horizontal FOV; −10° to +90° vertical FOV; mounted at 2.5 m	20 Hz, target 30,000 points/frame
CAV IMU	acceleration noise std. dev. 0.1 m/s²/axis; gyroscope noise std. dev. 0.002 rad/s/axis; gyro bias 0.001 rad/s/axis; seed 42	100 Hz, 100 records
CAV GNSS	latitude, longitude, and altitude noise std. dev. set to zero	10 Hz, 10 records
RSU RGB cameras	4 cardinal views: east, north, west, south; 1280 × 720; 110° horizontal FOV	20 Hz, 20 frames/view
RSU depth cameras	4 views co-located with the RSU RGB cameras; 1280 × 720; 110° horizontal FOV	20 Hz, 20 frames/view
RSU LiDAR	128 channels; 120 m range; 360° horizontal FOV; −50° to +50° vertical FOV	20 Hz, target 30,000 points/frame
RSU Radar	100 m range; 120° horizontal × 30° vertical FOV; yaw chosen to face the road	20 Hz, 10,000-point ray budget/frame; stored detections vary by scene
Birdview RGB	top-down camera at 130 m; 1280 × 1280; 90° FOV; yaw 0°	20 Hz, 20 frames

The 1-second recorded interval begins after a 0.3-second simulator warm-up. CARLA geometry/pose and CIR/CSI use a 2 kHz clock (2,000 samples), while the physical sensors retain the native rates above. Streams are therefore time-aligned but not rate-equal. Use each archive's carla/alignment_index.json: camera, LiDAR, Radar, and other sensor records are mapped to the nearest 2 kHz geometry/CSI frame. Do not assume that ordinal file indices from two modalities identify the same timestamp.

The LiDAR point counts are generation targets rather than a promise that every serialized cloud has exactly that size. Similarly, the Radar setting is a ray budget; CARLA writes only returned detections, so the number of stored Radar points depends on scene content. Per-frame poses and timestamps are stored with the sensor data, and the full machine-readable release policy is in metadata/generation_config.yaml.

Every formal sample passed CARLA trajectory and collision qualification before wireless ray tracing. The release contains no candidate samples, visual QA panels, videos, logs, cache keys, or editable simulator assets.

Packaged layout

To keep the Hub repository usable, each sample is distributed as one deterministic, uncompressed tar archive instead of hundreds of thousands of small files:

Scene2Wave/
├── README.md
├── LICENSE
├── ATTRIBUTION.md
├── THIRD_PARTY_NOTICES.md
├── CITATION.cff
├── DATA_PROVENANCE.md
├── dataset_info.json
├── schema.md
├── checksums.sha256
├── metadata/
│   ├── samples.jsonl
│   ├── source_samples.jsonl
│   ├── json_schema.json
│   ├── generation_config.yaml
│   └── raw_dataset_schema.md
└── data/main_training/<Town>/<state>/<profile>/<sample_id>.tar

Each tar has sample_metadata.json, carla/, and sionna/ at its member root. metadata/samples.jsonl adds archive_path, archive_bytes, and archive_sha256 to the public sample record. source_samples.jsonl preserves the original unpacked-tree index.

Load one sample without extracting the full dataset

import io
import json
import tarfile
from pathlib import Path

import numpy as np

root = Path("Scene2Wave")
records = [
    json.loads(line)
    for line in (root / "metadata/samples.jsonl").read_text().splitlines()
    if line.strip()
]
record = records[0]

with tarfile.open(root / record["archive_path"], mode="r:") as archive:
    member = archive.extractfile("./sample_metadata.json")
    metadata = json.load(member)

    npz_name = next(
        name for name in archive.getnames()
        if name.startswith("./sionna/") and name.endswith("_paths.npz")
    )
    payload = archive.extractfile(npz_name).read()
    with np.load(io.BytesIO(payload), allow_pickle=False) as arrays:
        print(record["sample_id"], arrays.files)

print(metadata["radio"]["carrier_frequency_hz"])

For repeated access, extract only the required sample:

mkdir sample
tar -xf data/main_training/Town01/10kmh/core/Req_Town01_dynamic_10kmh_t001_rfmain.tar -C sample

See schema.md for archive handling and metadata/raw_dataset_schema.md for the complete sensor, alignment, radar, and wireless field reference.

Integrity

checksums.sha256 covers every sample archive and all release metadata:

sha256sum --check checksums.sha256

Intended uses

• multimodal wireless-channel representation learning;
• CIR/CSI estimation and prediction;
• perception-to-channel feature extraction;
• synchronization, fusion, and robustness research;
• controlled comparisons across Town, speed, and propagation profile.

Limitations

• This is simulated data and does not reproduce every real sensor, material, traffic, weather, hardware, calibration, or regulatory condition.
• Only four CARLA Towns and the documented RF configurations are represented.
• Dataset splits must avoid leakage between closely related scenarios when evaluating generalization.
• Empty or outage links require explicit handling even though the formal release records valid CIR for all indexed samples.
• Users remain responsible for validating conclusions against real-world data.

License and third-party materials

This repository uses a layered license. To the extent Pengcheng Laboratory owns the applicable rights, Scene2Wave Project Content is licensed under CC BY 4.0. CARLA-derived elements and other third-party materials retain their respective terms. Read LICENSE, ATTRIBUTION.md, and THIRD_PARTY_NOTICES.md; the Hugging Face metadata therefore uses license: other rather than applying one license to every layer.

Citation

Until a dataset paper or DOI is assigned, cite this release using the metadata in CITATION.cff and include the repository URL and version. Preserve the author order listed above.