超大规模MIMO系统中稀疏度自适应的极化域信道估计  

Sparse-adaptive Polar-domain Channel Estimation for ExtremelyLarge-scale MIMO Systems

作　　者：卢嘉仪 雷浩 肖华华[2] 章嘉懿 LU Jiayi;LEI Hao;XIAO Huahua;ZHANG Jiayi(School of Electronic and Information Engineering,Beijing Jiaotong University,Beijing 100044,China;Zhongxing Telecom Equipment,Shenzhen 518057,China)

机构地区：[1]北京交通大学电子信息工程学院,北京100044 [2]中兴通讯股份有限公司,广东深圳518057

出　　处：《无线电通信技术》2023年第6期999-1005,共7页Radio Communications Technology

基　　金：国家自然科学基金(61971027)~~。

摘　　要：针对超大规模多输入多输出(Multiple-Input Multiple-Output, MIMO)系统中的极化域信道估计调整,提出了一种基于压缩感知的自适应极化域稀疏度同步正交匹配追踪(Adaptive Polar-domain Simultaneous Orthogonal Matching Pursuit, AP-SOMP)算法。AP-SOMP算法利用信道相关度设计合理的判决准则来估计极化域信道稀疏度,从而能在极化域信道稀疏度未知的情况下完成信道估计。该算法有效地克服了传统算法对信道稀疏度的依赖,具有更强的实用性。仿真结果表明,AP-SOMP算法在归一化均方误差性能表现上优于传统的基于网格的极化域同步正交匹配追踪(Polar-domain Simultaneous Orthogonal Matching Pursuit, P-SOMP)算法,且算法复杂度并未明显增加。An Adaptive Polar-domain Simultaneous Orthogonal Matching Pursuit(AP-SOMP)algorithm is proposed to handle the adjustments required for channel estimation in extremely large-scale Multiple-Input Multiple-Output(MIMO)systems.Unlike its counterparts,this innovative algorithm utilizes channel correlation information to design a decision criterion to estimate the degree of channel sparsity,which performs channel estimation without the prior knowledge of the channel sparsity in the polar-domain.AP-SOMP algorithm has been developed to effectively tackle the traditional reliance on channel sparsity level,demonstrating its higher practicality.Instead,enough simulation results show that AP-SOMP algorithm outperforms Polar-domain Simultaneous Orthogonal Matching Pursuit(P-SOMP)algorithm in terms of the normalized mean square error performance,while the complexity of the algorithm does not increase significantly.

关 键 词：信道估计 压缩感知 近场通信 超大规模多输入多输出 毫米波 

分 类 号：TN929.53[电子电信—通信与信息系统]