Enhancing Security in 5G NR With Channel-Robust RF Fingerprinting Leveraging SRS for Cross-Domain Stability

Abstract

Radio Frequency Fingerprinting (RFF) has emerged as a vital technique for enhancing Physical Layer Authentication (PLA) in New Radio (NR) networks. Unlike cryptographic methods, RFF leverages device-specific signal impairments to uniquely identify transmitters. Deep Learning (DL) advances have improved PLA, though challenges persist due to communication channel dynamics and device state changes. In this study, we propose a novel framework that integrates 5G NR protocol-specific structures and channel knowledge via SRS-based CSI to generate relative RFF features. Through a tailored frame design and carefully engineered processing pipeline, we achieve cross-domain stability and improved robustness against time-varying conditions. By applying regularization techniques (e.g., mixup) during training, our method further mitigates model overfitting and domain bias. Simulation and real-world SDR experiments, using data from 9 ADALM-PLUTO devices, validate the approach’s effectiveness. The proposed system attains recognition accuracies of 99.878%, 93.376%, 86.325%, and 66.558% in intra-domain, cross-channel, cross-time, and cross-scenario tests, respectively, highlighting its potential to substantially enhance physical layer security in NR-based networks.