Radio-Frequency Fingerprint Extraction Based on Feature Inhomogeneity

Abstract

With the popularization of the Internet of Things (IoT), its security has become increasingly prominent. Radio-frequency fingerprinting (RFF) is a promising approach to identify a specific emitter by extracting the intrinsic physical layer characteristics from transmitted signals, adopted as a lightweight noncryptographic access authentication technique. The realization of RFF relies on unintentional modulation of the pulse (UMOP). In this article, we discover the locality and inhomogeneity of RFF, that is, UMOP is concentrating in the radio-frequency fingerprint distribution subregion (RFDR), instead of evenly distributed over the entire feature. This property is demonstrated in the cepstral domain based on the transmitter distortion models. This is the first time that the inhomogeneity of UMOP and the cepstral domain analysis are employed in RFF. First, to automatically search the RFDRs, a method based on the dispersion characteristic index (DCI) and emitter-specific information index (ESI) is proposed, where DCI provides constraints on the scattering characteristics of RFF feature clusters and ESI evaluates the local differences of RFF features. Second, combining the RFDR selection method with the cepstrum analysis, a new feature CepH is obtained. Systematic experiments are conducted on the simulated data sets containing multiple modulation patterns and received data sets from multiple sources, which show that the RFDR is not affected by the modulation pattern and noise, and has high practicability. It also demonstrates that the proposed algorithm outperforms the state-of-art algorithms, especially in the case of a low signal-to-noise ratio (SNR). It can achieve a recognition accuracy of more than 90% under 0 dB.