Deep Learning-Based Symbol Detection for Time-Varying Nonstationary Channels  

作　　者：Xuantao Lyu Wei Feng Ning Ge Xianbin Wang 

机构地区：[1]Research Center of Networks and Communications,Peng Cheng Laboratory,Shenzhen 518055,China [2]Department of Electronic Engineering,Tsinghua University,Beijing 100084,China [3]Department of Electrical and Computer Engineering,The University of Western Ontario,London,ON N6A 5B9,Canada

出　　处：《China Communications》2022年第3期158-171,共14页中国通信（英文版）

基　　金：supported in part by the National Key R&D Program of China under Grant 2020YFA0711301;in part by the National Natural Science Foundation of China(No.61941104,62101292,61922049)。

摘　　要：The highly dynamic channel(HDC)in an extremely dynamic environment mainly has fast timevarying nonstationary characteristics.In this article,we focus on the most difficult HDC case,where the channel coherence time is less than the symbol period.To this end,we propose a symbol detector based on a long short-term memory(LSTM)neural network.Taking the sampling sequence of each received symbol as the LSTM unit's input data has the advantage of making full use of received information to obtain better performance.In addition,using the basic expansion model(BEM)as the preprocessing unit significantly reduces the number of neural network parameters.Finally,the simulation part uses the highly dynamic plasma sheath channel(HDPSC)data measured from shock tube experiments.The results show that the proposed BEM-LSTM-based detector has better performance and does not require channel estimation or channel model information.

关 键 词：highly dynamic channel deep neural network long short-term memory basis expansion model symbol detection 

分 类 号：TN911.7[电子电信—通信与信息系统] TP18[电子电信—信息与通信工程]