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pci
int64
rnti
int64
dl_cqi
int64
dl_ri
float64
dl_mcs
int64
dl_brate
string
dl_ok
int64
dl_nok
int64
dl_bler_pct
int64
dl_bs
int64
ul_pusch_snr
float64
ul_rsrp
float64
ul_ri
int64
ul_mcs
int64
ul_brate
string
ul_ok
int64
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int64
ul_bler_pct
int64
ul_bsr
int64
ul_ta
string
ul_phr
int64
1
4,601
15
1
12
13k
19
0
0
0
29.8
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1
26
40.5k
10
0
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0
222n
12
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4,601
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1
2
368
3
0
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27
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1
28
8.70k
2
0
0
0
211n
13
1
4,601
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0
27.3
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4.35k
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147n
23
1
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198n
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1
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224n
23
1
4,601
15
1
0
0
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28.6
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28
4.35k
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184n
23
1
4,601
15
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null
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214n
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211n
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160n
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28.1
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240n
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211n
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2
368
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8.70k
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27.9
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368
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23
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26.9
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227n
23
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4,601
15
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28
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4.35k
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23
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26.9
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4.35k
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4,601
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2
368
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28.4
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8.70k
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217n
23
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4,601
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27.3
-10.1
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28
8.70k
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201n
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1
4,602
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null
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27.5
-8.7
1
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8.70k
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20
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-48n
20
End of preview. Expand in Data Studio

Twin6G — Over-the-Air 5G NR PHY Dataset

Real over-the-air 5G NR measurements for the Twin6G project: raw radio-frequency samples and physical-layer telemetry of a live standalone 5G cell serving a commercial phone. Unlike simulator traces (for example UERANSIM), every recording here is a genuine radio observation, so it carries true physical-layer (Layer 1) information.

Each run is self-contained in a timestamped directory and provides two paired tiers captured in the same session:

  • Tier 1 — raw IQ: the rawest possible form, complex baseband samples of the cell sniffed off the air.
  • Tier 2 — PHY telemetry: per-UE radio metrics from the base station and radio measurements reported by the phone, plus the control-plane and MAC packet captures.

Provenance

All data is measured, over the air. It is collected on a software-defined radio testbed, not simulated. Each run directory carries a run_info.yaml and a timeline.ndjson with exact timing, hardware serials, configuration, and ground-truth events (attach, each IQ burst, teardown). The IQ and the base-station and phone metrics are simultaneous observations of the same live cell.

Runs

run_id duration band / carrier UE label
2026-10-15T1621Z_poco-n41-phy 20 min (2026-10-15 16:21:52–16:42:00 UTC) n41 TDD, ARFCN 518670 = 2593.35 MHz, 20 MHz, 30 kHz SCS Xiaomi Poco (COTS phone) benign single-cell baseline

Testbed for this run

  • Core: Open5GS (standalone 5G core), PLMN 99970.
  • gNB: srsRAN Project (release 25.10), PCI 1, on a USRP B210.
  • UE: a real Xiaomi Poco phone, SIM on PLMN 99970, attached over the air and kept RRC-connected with light background traffic.
  • Sniffer: a second USRP B210 tuned to the same 2593.35 MHz carrier, recording raw IQ while the cell ran.

Directory layout (per run)

<run_id>/
  tier1_iq/        raw IQ bursts (.sc16) + SigMF sidecars (.sigmf-meta)
  tier2_metrics/   gnb_sched_ue.csv, ue_nr_adb.csv, ue_adb.ndjson
  pcap/            gnb_mac.pcap (MAC-NR), gnb_ngap.pcap (NGAP + 5G NAS)
  logs/            srsRAN console + srslog, per-burst sniffer logs, phone radio logcat, keepalive ping
  config/          exact Open5GS core and srsRAN gNB configs used
  meta/            final size report
  run_info.yaml    run metadata
  timeline.ndjson  ground-truth event timeline (UTC)

Tier 1 — raw IQ (tier1_iq/)

Duty-cycled over-the-air capture of the live cell: a 2-second burst every 20 seconds across the whole run, so samples span the full session at a practical size. For the run above: 46 bursts, ~7.9 GB.

  • iq_<utc>_c<freq>_r<rate>.sc16 — interleaved signed 16-bit complex samples (I, Q, I, Q, …), little-endian. One 2-second burst = 46,080,000 complex samples = 184.32 MB. Sample rate 23.04 Msps, center frequency 2593.35 MHz.
  • iq_<utc>_c<freq>_r<rate>.sigmf-meta — a SigMF metadata sidecar (core:datatype: ci16_le, sample rate, center frequency, hardware serial, UTC datetime, label).

Read one burst in Python:

import numpy as np
x = np.fromfile("iq_….sc16", dtype=np.int16).astype(np.float32).view(np.complex64)
fs, fc = 23.04e6, 2593.35e6   # sample rate, center frequency

Typical uses: RF fingerprinting (carrier-frequency offset, IQ imbalance, phase noise), synchronization-signal (SSB / PSS-SSS) analysis, spectrograms, and physical-layer inputs to a digital-twin or anomaly model.

Tier 2 — PHY telemetry (tier2_metrics/, pcap/)

gnb_sched_ue.csv — per-UE physical-layer metrics from the base-station scheduler:

column meaning
pci, rnti cell identity, UE radio identifier
dl_cqi, dl_ri, dl_mcs, dl_brate downlink channel quality, rank, modulation-and-coding, bitrate
dl_ok, dl_nok, dl_bler_pct downlink HARQ ACK / NACK and block error rate
ul_pusch_snr, ul_rsrp uplink SNR and received power at the base station
ul_ri, ul_mcs, ul_brate, ul_ok, ul_nok, ul_bler_pct uplink rank, MCS, bitrate, HARQ, BLER
ul_bsr, ul_ta, ul_phr buffer status, timing advance, power headroom

ue_nr_adb.csv — the phone's own NR radio measurements, polled every 2 seconds over adb:

column meaning
ts, reg timestamp, registration state
ss_rsrp, ss_rsrq, ss_sinr synchronization-signal received power, quality, SINR (the UE's downlink view)
csi_rsrp, csi_rsrq, csi_sinr CSI-RS equivalents when reported

ue_adb.ndjson is the raw per-poll source for that CSV. The tier2_metrics/*.csv are derived from the raw logs and can be regenerated.

Packet captures (pcap/): gnb_mac.pcap is MAC-NR (per-transport-block MCS, grants, HARQ); gnb_ngap.pcap is NGAP control plane and contains the 5G NAS (5GMM) signaling, including the full registration sequence (Registration Request, Authentication Request/Response, Identity Request/Response, Security Mode Command). Read with Wireshark or tshark -r gnb_ngap.pcap -Y nas-5gs.

Loading in datasets

The IQ files are binary and are referenced by their SigMF sidecars; load them with NumPy as shown above. The CSV metrics are exposed as dataset-viewer configs:

from datasets import load_dataset
gnb = load_dataset("<your-username>/Twin6G", "gnb_metrics")  # per-UE PHY metrics
ue  = load_dataset("<your-username>/Twin6G", "ue_nr")        # phone-side NR measurements

Intended uses

  • Physical-layer feature extraction and RF fingerprinting of 5G base stations.
  • Training and validating digital-twin / anomaly models on real radio telemetry.
  • Fusing physical-layer features with higher-layer (NAS / RRC) detectors, for example to reduce false positives in fake-base-station detection.

Limitations and honest notes

  • Benign only. This run is a single legitimate cell. It is not a fake-base-station capture; that requires a separate two-transmitter benign-versus-fake handover scenario.
  • IQ is duty-cycled, 2 seconds every 20 seconds. It spans the full run but totals about 92 seconds of samples per 20-minute run, not continuous. This bounds storage and avoids radio contention with the live cell.
  • A minority of IQ bursts logged a transient hardware overflow (a few dropped samples at the start of the burst). Files are full-size and usable; the per-burst sniffer logs in logs/ record which bursts.
  • Identifiers are test values on a private PLMN (99970). Subscriber keys are not included; the UE config in config/ has credentials stripped.

Citation

Part of the Twin6G project (digital twin for 5G/6G). If you use this dataset, please cite the project and link this repository.

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