Specific Emitter Identification for IoT Devices Based on Deep Residual Shrinkage Networks

作     者：Peng Tang Yitao Xu Guofeng Wei Yang Yang Chao Yue Peng Tang;Yitao Xu;Guofeng Wei;Yang Yang;Chao Yue

作者机构：College of Communications EngineeringArmy Engineering University of PLANanjing 210007China 

出 版 物：《China Communications》 (中国通信（英文版）)

年 卷 期：2021年第18卷第12期

页      面：81-93页

核心收录：

学科分类：0810[工学-信息与通信工程] 08[工学] 0804[工学-仪器科学与技术] 

基　　金：the National Natural Science Foundation of China(No.U20B2038,No.61871398,NO.61901520 and No.61931011) the Natural Science Foundation for Distinguished Young Scholars of Jiangsu Province(No.BK20190030) the National Key R&D Program of China under Grant 2018YFB1801103. 

主　　题：specific emitter identification IoT de-vices deep learning soft threshold deep residual shrinkage networks 

摘      要：Specific emitter identification can distin-guish 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 extrac-tion is generally limited by prior professional knowl-edge.At the same time,it has been noted that the per-formance of most specific emitter identification meth-ods 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 imple-ment the classification of transmitters.Experimental results show that DRSN obtains the best accuracy un-der different SNRs and has less running time,which demonstrates the effectiveness of DRSN in identify-ing specific emitters.