RFSS: Multi-Standard RF Signal Source Separation Dataset

RFSS contains complex baseband mixtures of 2 to 4 simultaneous cellular signals (GSM, UMTS, LTE, 5G NR) together with per-source reference waveforms and full generation metadata. It is intended for blind, single-channel RF source separation research.

Paper: arXiv:2508.12106. A corrected version replaces the earlier ones and supersedes arXiv:2604.00398, whose results and dataset description contained errors. Code: https://github.com/chenhao1umbc/dataset_RFSS

Files

File	Samples	Size	Content
data/rfss_dataset.h5	100,000	about 103 GiB	2-, 3- and 4-source mixtures
data/rfss_single.h5	4,000	about 1.3 GiB	single-source reference samples
checkpoints/	15 files	about 0.8 GB	trained models of the paper (see Benchmark)

HDF5 layout (both files)

Dataset	Shape (multi-source file)	Type	Meaning
mixed_signals	(100000, 122880)	complex64	Received mixture, zero-padded after signal_lengths[i]
source_signals	(100000, 4, 122880)	complex64	Per-source reference, unused slots are all zero
signal_lengths	(100000,)	int32	Number of valid samples in the mixture of sample i
metadata	(100000,)	variable-length JSON string	Generation parameters (below)

Root attributes: actual_samples, max_samples, format (complex64), signal_duration_ms (1.0), creation_time, version (still 1.0; the files of release v1.1 are the same bytes as v1.0). Arrays are gzip-compressed, one sample per chunk.

Read a sample:

import h5py, json, numpy as np
with h5py.File("rfss_single.h5", "r") as f:
    i = 0
    L = int(f["signal_lengths"][i])
    mix = f["mixed_signals"][i, :L]
    srcs = f["source_signals"][i]          # (4, 122880), unused rows are zero
    meta = json.loads(f["metadata"][i])

Remote access without downloading everything: use HTTP range reads through huggingface_hub or fsspec; each sample is one compressed chunk.

Important properties of the data (read before using)

Updated 2026-10-03 after the Builder's forward-model check (check/verify_reference_alignment.py, check/reference_alignment_results.json).

1. Sample rates and lengths vary. Each source is generated at its native 3GPP rate (GSM 2.166 MHz, UMTS 7.68 MHz, LTE 1.92-30.72 MHz, 5G NR 15.36-122.88 MHz) for about 1 ms. All sources of one sample are resampled to a common mixture rate equal to the highest source rate in that sample. signal_lengths therefore ranges from 1,890 to 122,880.
2. Native length is not exactly 1 ms for every standard. GSM sources hold 1,890 samples (nominal 2,166). 5G NR sources are slightly shorter than nominal (for example 122,696, 122,640, 61,348 and 30,660 samples; nominal 122,880, 61,440 and 30,720). LTE and UMTS are exact. Find a source's true length as the index of its last non-zero sample plus one. Do not assume round(sample_rate * 0.001).
3. What source_signals holds. Each stored source is the waveform after its TDL channel and hardware impairments (CFO, SFO, I/Q imbalance, DC offset, phase noise, PA nonlinearity), at its native rate and zero-padded. It is before resampling to the mixture rate, before the adjacent-channel frequency shift, before power scaling, and before AWGN. It is not the clean transmitted waveform.
4. Aligned reference (what the mixture actually contains). The term each source contributes to mixed_signals is normalize_power( freq_shift( pad( resample( source ) ) ), power_ratios_db[i] ), where freq_shift is applied only in adjacent-channel mode using mixing_params.frequency_offsets_hz. Rebuilding the noiseless mixture this way with SignalMixer matched the stored mixture to within the expected AWGN level in 204 of 204 checked test samples. Use the aligned reference for scoring separation methods. Reference implementation: build_aligned_references(source_block, meta, signal_len) in src/utils_mixing.py of the GitHub repo. Independently checked by the Reviewer on 40 random test samples read from this repository (median residual gap 0.017 dB, maximum 0.167 dB versus the stored SNR).
5. Do not score against the raw stored sources in adjacent-channel mixtures. The unshifted reference scores a median of about -40 dB against the mixture, versus about -6 dB for the aligned reference (102 adjacent-channel test samples).
6. Power scaling. Sources are scaled by mixing_params.power_ratios_db (examples reach +/-25 dB), so the mixture power can be far above the stored reference power. SI-SINR is scale invariant.
7. MIMO field is vestigial. metadata.mimo_config reports 1x1, 2x2 or 4x4, but no MIMO processing is applied in the generator. All mixtures are single-stream (SISO). Ignore this field.

Metadata schema

Per sample: sample_id, seed, num_sources, snr_db, generation_time, mimo_config (num_tx, num_rx, spatial_correlation), mixing_params (num_sources, mixing_mode in {co-channel, adjacent-channel, single}, power_ratios_db, frequency_offsets_hz), and sources: a list with, per source, standard, signal_params (bandwidth, modulation, sample_rate, numerology for NR), channel_params (tdl_model in TDL-A..E, doppler_hz) and impairment_params (mode in {clean, single, multiple}, cfo_ppm, sfo_ppm, iq_amp_db, iq_phase_deg, dc_offset_dbc, phase_noise_dbc_hz, pa_backoff_db).

Generation summary (from the paper source and the 20,000-sample coverage check)

• Source counts: 2 / 3 / 4 sources with target weights 0.50 / 0.35 / 0.15 (realized about 0.50 / 0.35 / 0.15).
• Mixing mode: about 40% co-channel, 60% adjacent-channel. In adjacent-channel mode source k of N is shifted by (k - floor(N/2)) x 2 MHz for every standard and no adjacent-channel filtering is modelled, so most mixtures still contain overlapping bands (97% of the two-source adjacent-channel test samples). For the 1.8% of adjacent-channel samples whose mixture rate is below 4 MHz (GSM and narrow LTE sources only) the shift exceeds the Nyquist frequency and the shifted source wraps around in frequency; the aligned references apply the same wrap, so the labels stay exact.
• Channels: 3GPP TDL-A to TDL-E (TR 38.901), weighted selection, Jakes fading with Doppler up to 700 Hz.
• Noise: AWGN, SNR from -10 to 40 dB (observed mean about 12 dB).
• Hardware impairments per source: CFO, SFO, I/Q imbalance, DC offset, phase noise, PA nonlinearity (Rapp model); about 20% of sources clean, 30% one impairment, 50% several.
• Standards share in the scanned subset: 5G NR 0.38, LTE 0.37, GSM 0.12, UMTS 0.12.

Splits

By sample index in rfss_dataset.h5: train 0-69,999; validation 70,000-84,999; test 85,000-99,999. The source-count shares are the same in the three splits (2 / 3 / 4 sources: 50.2 / 35.5 / 14.3 % of the test split, 49.9 / 35.1 % for 2 / 3 sources in the training split).

Benchmark

Phase-sensitive permutation-invariant SI-SINR (Le Roux et al., 2019) on the test split, one random 7,680-sample window per sample, reported as the gain over the input mixture in dB. 2-source: mean of 3 training seeds, n = 7,526 test mixtures; 3- and 4-source: one seed, n = 5,324 and 2,150. Model gains with 95 % bootstrap intervals, the seed spread and the paired differences are in the paper; every number comes from the JSON files under check/ of the GitHub repository (eval_all_src*_crop0_frozen_results.json).

Method	2 sources	3 sources	4 sources
STFT-BLSTM (7.4 M parameters)	+6.14	+5.14	+4.52
DPRNN (1.1 M)	+5.85	+5.03	+3.44
Conv-TasNet (2.5 M)	+5.61	+4.77	+3.54
IRM oracle	+8.04	+9.61	+10.35
Noise-limited oracle	+10.16	+11.53	+12.13
NMF	-1.24	-0.57	-0.05
FastICA	-13.60	-11.09	-9.49

The checkpoints/ folder holds the 15 models scored in these tables, stored as checkpoints/<run>/ckpt/<file>.pt. Moving checkpoints to final in a clone of the GitHub repository lets check/run_test_passes.sh score them.

Known limitations

• Single-antenna (SISO) mixtures only; mimo_config in the metadata is not used.
• Downlink waveforms only; no uplink, NB-IoT, LTE-M or sidelink.
• Synthetic TDL channels, no measured channel data.
• Absolute separation scores of current methods are low; the dataset is hard for the baselines tested.
• Version history: see the next section.

Version history

• v1.0 (git tag, February 2026): the two HDF5 files, without a dataset card.
• v1.1 (this card): the HDF5 files are byte-identical to v1.0 (SHA-256 of both files checked against the uploaded ones). What changed is the documentation and what is released with it: the reference definition above (what source_signals holds and how to rebuild the aligned reference), the adjacent-channel convention (a fixed 2 MHz spacing, no adjacent-channel filtering, so most adjacent-channel mixtures still overlap), the vestigial mimo_config field, the split indices, the evaluation protocol, the trained checkpoints and the licence. Earlier descriptions of the dataset (arXiv:2508.12106 v1 and arXiv:2604.00398) are wrong where they differ from this card.

License and citation

Data: CC BY-NC 4.0, free for everyone to use and share with attribution, not for commercial use. Code (GitHub repository): PolyForm Noncommercial 1.0.0.

@article{chen2026rfss,
  title   = {{RFSS}: A Multi-Standard {RF} Signal Source Separation Dataset with 3GPP-Standardized Channel and Hardware Impairments},
  author  = {Chen, Hao and Jin, Rui and Tan, Dayuan},
  journal = {arXiv preprint arXiv:2508.12106},
  year    = {2026}
}