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 

机构地区：[1]School of Informatics,Xiamen University,Xiamen 361005,China [2]National Mobile Communications Research Laboratory,Southeast University,Nanjing 210096,China [3]the Information Engineering,University of the Witwatersrand,Johannesburg 2050,South Africa

出　　处：《Intelligent and Converged Networks》2023年第1期1-14,共14页智能与融合网络（英文）

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).

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,etc.To 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 accuracy.Moreover,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 improved.Simulation 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.

关 键 词：Orthogonal Frequency Division Multiplexing(OFDM) Underwater Acoustic Communications(UAC) sparse recovery deep learning sparse learning DENOISING approximate message passing 

分 类 号：TN929.5[电子电信—通信与信息系统] TP183[电子电信—信息与通信工程]