Model-Driven Radio Frequency Fingerprint Identification Based on Hardware Impairments

The proliferation of the Internet of Things (IoT) necessitates advanced physical layer authentication (PLA) techniques. While radio frequency fingerprint identification (RFFI) leverages hardware impairments for device authentication, existing data-driven approaches face challenges such as heavy data dependency, limited interpretability, and sensitivity to channel variations. To address these issues, this paper proposes a model-driven RFFI system based on hardware impairments, which integrates a radio frequency fingerprint (RFF) extractor with a support vector machine (SVM) classifier. Inspired by the LS parameter estimation technique, the RFF extractor associates the physical model with hardware characteristics, constructing an interpretable RFF database. Compared to the benchmark scheme, our proposed scheme achieves higher classification accuracy with limited training samples and exhibits strong robustness against channel effects. Experimental results validate the superiority of system performance in device classification and anomaly detection. Simulation results demonstrate that the proposed RFFI system significantly reduces data dependency while maintaining reliability in dynamic IoT environments.