Using a 1D-ResNet with squeeze-and-excitation blocks on 2531-dimensional features from 5G NR SRS, the system achieves 3.92% equal error rate for distinguishing legitimate from spoofed probes on real over-the-air data with chronological splitting.
Physical layer security for next generation wireless networks,
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Deep Learning-Based Physical Layer Authentication Using 5G NR Sounding Reference Signals: A Temporal Generalization Study on Real Testbed Data
Using a 1D-ResNet with squeeze-and-excitation blocks on 2531-dimensional features from 5G NR SRS, the system achieves 3.92% equal error rate for distinguishing legitimate from spoofed probes on real over-the-air data with chronological splitting.