Concentrate on Hardware Imperfection via Aligning Reconstructed States

Abstract

Specific emitter identification (SEI) is to authenticate communication devices via analyzing unintentional modulation (UIM) from hardware imperfection. A great challenge existing in current methods is the interference of intentional modulation (IM) and channel noise. To address this issue, this letter firstly analyzes how IM, UIM, and additive noise (AN) affect reconstructed states, which describes the dynamic characteristics of communication devices. The findings indicate that IM and UIM bring a scaling transform, while AN corresponds to a translation transform. Based on theoretical analysis, a novel SEI method concentrating on UIM is proposed. An advanced PointNet architecture is designed with stacked spatial transformer networks (STN), namely PointNet-Alignment Point by Point (PointNet-APBP). PointNet-APBP aligns reconstructed states into a canonical space and extracts spatial interaction as fingerprint features. Simulated and real-world experiments validate that the proposed method can mitigate the effects of IM as well as AN, concentrate feature extractors on UIM, and in turn achieve higher accuracy and stronger robustness.