Acquisition of channel state information for mmWave massive MIMO: traditional and machine learning-based approaches

作     者：Chenhao QI Peihao DONG Wenyan MA Hua ZHANG Zaichen ZHANG Geoffrey Ye LI Chenhao QI;Peihao DONG;Wenyan MA;Hua ZHANG;Zaichen ZHANG;Geoffrey Ye LI

作者机构：School of Information Science and EngineeringSoutheast University College of Electronic and Information EngineeringNanjing University of Aeronautics and Astronautics Department of Electrical and Electronic EngineeringImperial College London 

出 版 物：《Science China(Information Sciences)》 (中国科学:信息科学(英文版))

年 卷 期：2021年第64卷第8期

页      面：5-20页

核心收录：

学科分类：080904[工学-电磁场与微波技术] 12[管理学] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0810[工学-信息与通信工程] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 080402[工学-测试计量技术及仪器] 0804[工学-仪器科学与技术] 081001[工学-通信与信息系统] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by National Natural Science Foundation of China (Grant Nos. 62071116  61960206005) 

主　　题：beam training channel estimation machine learning massive MIMO millimeter wave (mmWave) communications 

摘      要：The accuracy of channel state information(CSI) acquisition directly affects the performance of millimeter wave(mmWave) communications. In this article, we provide an overview on CSI acquisition,including beam training and channel estimation for mmWave massive multiple-input multiple-output *** beam training can avoid the estimation of a high-dimension channel matrix, while the channel estimation can flexibly exploit advanced signal processing techniques. In addition to introducing the traditional and machine learning-based approaches in this article, we also compare different approaches in terms of spectral efficiency, computational complexity, and overhead.