| --- |
| license: mit |
| pretty_name: Twin6G — Over-the-Air 5G NR PHY Dataset |
| tags: |
| - 5g |
| - 5g-nr |
| - physical-layer |
| - rf |
| - iq |
| - sigmf |
| - srsran |
| - open5gs |
| - ran-telemetry |
| - wireless |
| - digital-twin |
| - fake-base-station |
| size_categories: |
| - 1K<n<10K |
| configs: |
| - config_name: gnb_metrics |
| data_files: "**/tier2_metrics/gnb_sched_ue.csv" |
| - config_name: ue_nr |
| data_files: "**/tier2_metrics/ue_nr_adb.csv" |
| --- |
| |
| # 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](https://github.com/sigmf/SigMF) metadata sidecar |
| (`core:datatype: ci16_le`, sample rate, center frequency, hardware serial, UTC datetime, label). |
|
|
| Read one burst in Python: |
|
|
| ```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: |
|
|
| ```python |
| 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. |
|
|
