Massive MIMO系统基于子空间的半盲信道估计  

Subspace-based semi-blind channel estimation for Massive MIMO systems

作　　者：王茜竹[1] 邱聪聪[1] WANG Qianzhu;QIU Congcong(School of Communication and Information Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065,China)

机构地区：[1]重庆邮电大学通信与信息工程学院,重庆400065

出　　处：《计算机工程与应用》2018年第8期91-95,共5页Computer Engineering and Applications

基　　金：国家科技重大专项资助项目(No.2016ZX03002019-007-001);重庆市教委科学技术研究项目(No.KJ1500427)

摘　　要：针对大规模MIMO系统中存在的导频污染问题,结合目前研究的基于奇异值(SVD)分解的信道估计算法,在考虑到该算法中的协方差矩阵是用有限的样本数据代替真实数据必然存在偏差的问题,给出了一种联合ILSP(Iterative Least Square with Projection)的基于SVD的半盲信道估计算法。仿真结果表明改进后的信道估计算法能够有效减小已有算法中存在的偏差问题,提高信道估计精确度,有效减轻导频污染给大规模MIMO系统带来的影响,从而实现大规模MIMO系统性能的提升。In view of the pilot contamination problems in Massive MIMO systems,together with the present study of SVD-based channel estimation algorithm,because the covariance matrix is made of the finite sample data rather than real data,it is a deviation here.Thus,joint SVD-based method and Iterative Least Square with Projection(ILSP)algorithm is introduced.Simulation results indicate that the proposed method can effectively reduce the deviation and improve the channel estimation accuracy,and it can effectively mitigate the effect of pilot contamination,so as to realize the purpose of Massive MIMO system performance improvement.

关 键 词：大规模多输入多输出(MIMO)系统 导频污染 奇异值分解(SVD)算法 迭代最小二乘投影(ILSP)算法 

分 类 号：TN929.5[电子电信—通信与信息系统]