毫米波超大规模MIMO信道估计算法研究  

Research on Millimeter Wave Ultra Massive MIMO Channel Estimation

作　　者：朱璇 金锡嘉 ZHU Xuan;JIN Xijia(School of Internet of Things Engineering,Jiangnan University,Wuxi 214122,China)

机构地区：[1]江南大学物联网工程学院,江苏无锡214122

出　　处：《无线电通信技术》2023年第3期404-409,共6页Radio Communications Technology

基　　金：中国科学院上海微系统与信息技术研究所无线传感网与通信重点实验室开放课题(20190917)。

摘　　要：超大规模多输入多输出(Multiple-Input Multiple-Output, MIMO)凭借可靠性强、鲁棒性好、频谱利用率和传输容量高等优势成为6G系统研究的热点之一。就毫米波(millimeter Wave, mmWave)大规模MIMO的信道估计技术展开研究,并对其简要分类。介绍了经典的信道估计算法如最小二乘、最小均方误差算法,论述了基于压缩感知的信道估计算法,基于波束训练以及深度学习的信道估计算法;并且通过仿真实验验证了路径数目和天线数目对OMP算法和ADMM算法NMSE性能的影响;对信道估计技术的未来做出展望,包括高移动性应用场景、普适性的深度学习算法、与智能反射表面结合、与非正交多址接入结合等。Ultra massive Multiple-Input Multiple-Output(MIMO)has become one research hotspot of 6G system due to its advantages of strong reliability,good robustness,high spectrum utilization rate,and high transmission capacity.In this paper,the channel estimation technique of millimeter Wave(mmWave)large-scale MIMO is studied and briefly classified.Firstly,classical channel estimation algorithms such as least squares and least mean square error are introduced.Then,channel estimation algorithms based on compressed sensing,beam training and deep learning are discussed.Furthermore,simulation experiments verify the influence of the number of paths and antennas on the NMSE performance of OMP algorithm and ADMM algorithm.Finally,the future of channel estimation technology is prospected,including high mobility application scenarios,universal deep learning algorithm,combination with intelligent reflective surface,and non-orthogonal multiple access.

关 键 词：毫米波 信道估计 压缩感知 波束训练 深度学习 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]