Deep Learning-Based Physical Layer Authentication Using 5G NR Sounding Reference Signals: A Temporal Generalization Study on Real Testbed Data
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866917401159794688 |
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| author | Mallikarjun, Sachinkumar B. Reski, Marvin Weinand, Andreas Schotten, Hans D. |
| author_facet | Mallikarjun, Sachinkumar B. Reski, Marvin Weinand, Andreas Schotten, Hans D. |
| contents | Physical Layer Authentication (PLA) exploits the spatial uniqueness of wireless channel characteristics in order to authenticate devices without recourse to higher-layer cryptographic protocols, which remain vulnerable to key compromise. This paper reports a comprehensive PLA system constructed on 5G New Radio (NR) Sounding Reference Signals (SRS) extracted from a real OpenAirInterface (OAI) testbed operating in band n78 (3.5 GHz) with 40 MHz bandwidth and 30 kHz subcarrier spacing. The proposed approach extracts a 2,531-dimensional feature vector per SRS probe, combining per-subcarrier channel state information (1,248 amplitude and 1,247 differential-phase coefficients), power delay profile taps, delay spread, Doppler statistics, and nonlinear dynamics indicators. A deep one-dimensional Residual Network (1D-ResNet) augmented with Squeeze-and-Excitation (SE) attention blocks is employed to classify each probe as either legitimate or spoofed. Evaluation is conducted on 20,317 over-the-air SRS probes acquired across four measurement sessions using a USRP B210 software-defined radio as the legitimate device and a commercial mobile handset as the attacker. Under a strict chronological train/validation/test split that eliminates temporal leakage, an Equal Error Rate (EER) of 3.92% is attained, with AUC = 0.962 on the held-out test set, and an authentication latency of less than 0.1 ms per probe, which is compatible with 5G Ultra-Reliable Low-Latency Communications (URLLC) requirements. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_10327 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Deep Learning-Based Physical Layer Authentication Using 5G NR Sounding Reference Signals: A Temporal Generalization Study on Real Testbed Data Mallikarjun, Sachinkumar B. Reski, Marvin Weinand, Andreas Schotten, Hans D. Signal Processing Physical Layer Authentication (PLA) exploits the spatial uniqueness of wireless channel characteristics in order to authenticate devices without recourse to higher-layer cryptographic protocols, which remain vulnerable to key compromise. This paper reports a comprehensive PLA system constructed on 5G New Radio (NR) Sounding Reference Signals (SRS) extracted from a real OpenAirInterface (OAI) testbed operating in band n78 (3.5 GHz) with 40 MHz bandwidth and 30 kHz subcarrier spacing. The proposed approach extracts a 2,531-dimensional feature vector per SRS probe, combining per-subcarrier channel state information (1,248 amplitude and 1,247 differential-phase coefficients), power delay profile taps, delay spread, Doppler statistics, and nonlinear dynamics indicators. A deep one-dimensional Residual Network (1D-ResNet) augmented with Squeeze-and-Excitation (SE) attention blocks is employed to classify each probe as either legitimate or spoofed. Evaluation is conducted on 20,317 over-the-air SRS probes acquired across four measurement sessions using a USRP B210 software-defined radio as the legitimate device and a commercial mobile handset as the attacker. Under a strict chronological train/validation/test split that eliminates temporal leakage, an Equal Error Rate (EER) of 3.92% is attained, with AUC = 0.962 on the held-out test set, and an authentication latency of less than 0.1 ms per probe, which is compatible with 5G Ultra-Reliable Low-Latency Communications (URLLC) requirements. |
| title | Deep Learning-Based Physical Layer Authentication Using 5G NR Sounding Reference Signals: A Temporal Generalization Study on Real Testbed Data |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2604.10327 |