The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
~~~~~~~~~~~~~~~~~~~~~~~~~^
StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/hdf5/hdf5.py", line 49, in _split_generators
import h5py
ModuleNotFoundError: No module named 'h5py'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 71, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
~~~~~~~~~~~~~~~~~~~~~~~^
path=dataset,
^^^^^^^^^^^^^
config_name=config,
^^^^^^^^^^^^^^^^^^^
token=hf_token,
^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
path,
...<6 lines>...
**config_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
OAI 5G B210 PLKG Dataset
Single-UE (B210) capture campaign for the channel-informed neural PHY
key-generation / RF-fingerprinting project. This is a device-specific
companion dataset to
ahancock516/oai-5g-plkg-dataset —
it isolates data from a single B210 UE so it can be combined later with
equivalent captures from other UE hardware (additional single-UE datasets
to follow) into a multi-device dataset for validating IMSI/RNTI labeling
and RF fingerprinting across device types.
Deployment
- OAI 5G NR SA: X410 gNB + B210 UE
- Band n78, 51 PRB, 30 kHz SCS (numerology 1), DDSUU TDD pattern
- gNB: DGX Spark (ARM64) host; UE: separate x86_64 host, same LAN
Fixed-PRB allocation (both directions target 20 PRB / 240 subcarriers)
- UL (PUSCH): every scheduled grant — including Msg3 — is
unconditionally forced to 20 PRB (
FIXED_PUSCH_RB_SIZEin the gNB MAC scheduler). 100% of captured UL records measurerb_size == 20. - DL (PDSCH): forced to 20 PRB only for grants actually carrying
DTCH (user-plane) data, detected per-slot from RLC buffer status
(
sched_ctrl->rlc_status) immediately before scheduling. Control-plane signaling (registration, PDU Session Resource Setup, RRC Reconfiguration, Msg3/Msg4 RA procedure) is left at its natural, unconstrained size — an earlier unconditional version of this patch silently starved those larger control messages and broke PDU session establishment. 99.6% of captured DL records measurenb_rbs == 20; the remainder are natural-sized control-plane grants (mostly 5 PRB).
Traffic generation
Captures were generated with sustained bidirectional iperf3 traffic
(--bidir, UDP, 5 Mbit/s each direction) between the UE and the core
network's external data network host, rather than repeated attach/detach
cycling — this produces continuous real DTCH data grants on both UL and
DL simultaneously, instead of relying on registration-phase signaling
alone.
Contents
pusch/pusch_dataset_b210_bidir_20prb.bin # UL captures, format v5
pdsch/pdsch_dataset_b210_bidir_20prb.bin # DL captures, format v4
paired/paired_b210_bidir_20prb.h5 # UL/DL reciprocity pairs
Binary formats (PUSC/PDSC magic, versioned header) are documented in
the respective capture-plugin repos:
nr_pusch_capture,
nr_pdsch_capture.
Both plugins write a wall-clock timestamp_ns (CLOCK_REALTIME) per
record, used for pairing.
Pairing method
Built with build_paired_dataset.py
(--pairing-method timestamp_primary --comparison-domain delay): finds
each UL capture's nearest DL capture by real timestamp per IMSI (not
frame/slot distance, which is unreliable across the 10.24s SFN wrap
period), then compares full-bandwidth delay-domain |h(tau)| rather
than frequency-domain H(f) — valid even when the UL/DL grants share no
overlapping subcarriers, which fixed-size scheduling makes common.
Known caveats
- Yield: 70,498 pairs from 77,787 UL / 64,991 DL captures — the highest yield observed in this project to date, attributable to sustained bidirectional traffic keeping UL and DL activity continuously co-occurrent in time (vs. registration-only cycling, where pairs only arrive in bursts around attach events).
- Reciprocity correlation: delay-domain magnitude correlation on a 3,000-pair sample: median 0.679 (mean 0.683, std 0.098; p10=0.559, p90=0.813). This is a real, non-degenerate reciprocity signal, but below the best value previously recorded on this rig (median 0.84, from a session with specifically re-tuned antenna positioning/gain). This capture was not re-tuned before the session — treat 0.68 as this session's as-found RF condition, not a ceiling.
- Cross-host clock sync: both hosts are individually NTP-synced, but
to different upstream sources — the gNB host to a public NTP pool
(
1-2ms RMS offset), the UE host to a local LAN NTP server (10µs RMS offset). Cross-host timestamp alignment is bounded by the gNB host's accuracy, not sub-millisecond, even though the pairing search window is ms-scale. - K-factor / residual-entropy check not yet applied. High UL/DL correlation is necessary but not sufficient for good key-generation material — a channel dominated by one strong deterministic (e.g. line-of-sight) component can correlate well while still being low-entropy and potentially predictable to an eavesdropper. This check is still open project work, not yet run against this dataset.
Intended use
- UL split (
pusch/): shared with a collaborator for RF fingerprinting model training (independent of the reciprocity/pairing work above). - Full dataset: validating IMSI-to-RNTI labeling and reciprocity pipeline correctness on a single, known device, ahead of combining with other UE hardware into a multi-device dataset.
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