Silhouette Score Efficient Radio Frequency Fingerprint Feature Extraction

Radio frequency fingerprint (RFF) identification technology, which exploits relatively stable hardware imperfections, is highly susceptible to constantly changing channel effects. Although various channel-robust RFF feature extraction methods have been proposed, they predominantly rely on experimental comparisons rather than theoretical analyses. This limitation hinders the progress of channel-robust RFF feature extraction and impedes the establishment of theoretical guidance for its design. In this paper, we establish a unified theoretical performance analysis framework for different RFF feature extraction methods using the silhouette score as an evaluation metric, and propose a precoding-based channel-robust RFF feature extraction method that enhances the silhouette score without requiring channel estimation. First, we employ the silhouette score as an evaluation metric and obtain the theoretical performance of various RFF feature extraction methods using the Taylor series expansion. Next, we mitigate channel effects by computing the reciprocal of the received signal in the frequency domain at the device under authentication. We then compare these methods across three different scenarios: the deterministic channel scenario, the independent and identically distributed (i.i.d.) stochastic channel scenario, and the non-i.i.d. stochastic channel scenario. Finally, simulation and experimental results demonstrate that the silhouette score is an efficient metric to evaluate classification accuracy. Furthermore, the results indicate that the proposed precoding-based channel-robust RFF feature extraction method achieves the highest silhouette score and classification accuracy under channel variations.