Denoising enabled channel estimation for underwater acoustic communications:A sparsity-aware model-driven learning approach

作     者：Sicong Liu Younan Mou Xianyao Wang Danping Su Ling Cheng 

作者机构：School of InformaticsXiamen UniversityXiamen 361005China National Mobile Communications Research LaboratorySoutheast UniversityNanjing 210096China the Information EngineeringUniversity of the WitwatersrandJohannesburg 2050South Africa 

出 版 物：《Intelligent and Converged Networks》 (智能与融合网络（英文）)

年 卷 期：2023年第4卷第1期

页      面：1-14页

核心收录：

学科分类：0710[理学-生物学] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Natural Science Foundation of China(No.61901403) the Science and Technology Key Project of Fujian Province,China(Nos.2021HZ021004 and 2019HZ020009) the Open Research Fund of National Mobile Communications Research Laboratory,Southeast University(No.2023D10) the Youth Innovation Fund of Natural Science Foundation of Xiamen(No.3502Z20206039) the Science and Technology Key Project of Xiamen(No.3502Z20221027) the Xiamen Special Fund for Marine and Fishery Development(No.21CZB011HJ02) 

主　　题：Orthogonal Frequency Division Multiplexing(OFDM) Underwater Acoustic Communications(UAC) sparse recovery deep learning sparse learning denoising approximate message passing 

摘      要：It has always been difficult to achieve accurate information of the channel for underwater acoustic communications because of the severe underwater propagation conditions,including frequency-selective property,high relative mobility,long propagation latency,and intensive ambient noise,*** this end,a deep unfolding neural network based approach is proposed,in which multiple layers of the network mimic the iterations of the classical iterative sparse approximation algorithm to extract the inherent sparse features of the channel by exploiting deep learning,and a scheme based on the Sparsity-Aware DNN(SA-DNN)for UAC estimation is proposed to improve the estimation ***,we propose a Denoising Sparsity-Aware DNN(DeSA-DNN)based enhanced method that integrates a denoising CNN module in the sparsity-aware deep network,so that the degradation brought by intensive ambient noise could be eliminated and the estimation accuracy can be further *** results demonstrate that the performance of the proposed schemes is superior to the state-of-the-art compressed sensing based and iterative sparse recovery schems in the aspects of channel recovery precision,pilot overhead,and robustness,particularly under unideal circumstances of intensive ambient noise or inadequate measurement pilots.