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Modulation Recognition in Maritime Multipath Channels:A Blind Equalization-Aided Deep Learning Approach

查看全文 作　　者：Xuefei [1]Ji;Jue [1,2]Wang;Ye [1,2]Li;Qiang [1]Sun;Chen [1]Xu 高影响力作者 机构地区：[1]School of Information Science and Technology,Nantong University,Nantong 226019,China;[2]Research Center of Networks and Communications,Peng Cheng Laboratory,Shenzhen 518040,China高影响力机构 出　　处：《China Communications》索引2020年第17卷第3期,共14页高影响力期刊 基　　金：the National Natural Science Foundation of China under Grant 61771264,61801114,61501264,61771286;the Nantong University-Nantong Joint Research Center for Intelligent Information Technology under Grant No.KFKT2017B01,KFKT2017A04;the Natural Science Foundation of Jiangsu Province under Grant BK20170688. 摘　　要：Modulation recognition has been long investigated in the literature,however,the performance could be severely degraded in multipath fading channels especially for high-order Quadrature Amplitude Modulation(QAM)signals.This could be a critical problem in the broadband maritime wireless communications,where various propagation paths with large differences in the time of arrival are very likely to exist.Specifically,multiple paths may stem from the direct path,the reflection paths from the rough sea surface,and the refraction paths from the atmospheric duct,respectively.To address this issue,we propose a novel blind equalization-aided deep learning(DL)approach to recognize QAM signals in the presence of multipath propagation.The proposed approach consists of two modules:A blind equalization module and a subsequent DL network which employs the structure of ResNet.With predefined searching step-sizes for the blind equalization algorithm,which are designed according to the set of modulation formats of interest,the DL network is trained and tested over various multipath channel parameter settings.It is shown that as compared to the conventional DL approaches without equalization,the proposed method can achieve an improvement in the recognition accuracy up to 30%in severe multipath scenarios,especially in the high SNR regime.Moreover,it efficiently reduces the number of training data that is required. 关 键 词：MODULATION RECOGNITION DEEP learning BLIND EQUALIZATION
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