Specific emitter identification for IoT devices based on deep residual shrinkage networks

Abstract

Specific emitter identification can distinguish individual transmitters by analyzing received signals and extracting inherent features of hard-ware circuits. Feature extraction is a key part of traditional machine learning-based methods, but manual extraction is generally limited by prior professional knowledge. At the same time, it has been noted that the performance of most specific emitter identification methods degrades in the low signal-to-noise ratio (SNR) environments. The deep residual shrinkage network (DRSN) is proposed for specific emitter identification, particularly in the low SNRs. The soft threshold can preserve more key features for the improvement of performance, and an identity shortcut can speed up the training process. We collect signals via the receiver to create a dataset in the actual environments. The DRSN is trained to automatically extract features and implement the classification of transmitters. Experimental results show that DRSN obtains the best accuracy under different SNRs and has less running time, which demonstrates the effectiveness of DRSN in identifying specific emitters.