采用数据辅助的大规模MIMO信道半盲迭代估计  

Semi-blind Iterative Estimation of Massive MIMO Channel with Data Assistance

作　　者：何文旭 张静[1] 马惠艳 HE Wenxu;ZHANG Jing;MA Huiyan(College of Information,Mechanical and Electronic Engineering,Shanghai Normal University,Shanghai 200234,China)

机构地区：[1]上海师范大学信息与机电工程学院,上海200234

出　　处：《电讯技术》2021年第2期197-202,共6页Telecommunication Engineering

基　　金：国家自然科学基金青年基金项目(61701307);上海师范大学校级一般项目(KF201820)。

摘　　要：由于大规模多输入多输出(Multiple-Input Multiple-Output,MIMO)信道衰落参数的维度较高,导致最优估计算法计算量大且需要的导频数较多而影响到频谱效率。为降低计算复杂度并减少导频开销,提出了两种基于期望最大化(Expectation Maximization,EM)估计的半盲迭代改进算法。利用少量正交导频序列估计出信道初值,通过用户与基站间信道的大尺度衰落系数把用户分簇,根据这些系数按比例地分配接收噪声,再利用数据的统计特性推导出信道衰落参数的均值和方差。仿真结果表明,当导频数远少于待估计参数的个数时,半盲估计算法的均方误差(Mean Square Error,MSE)优于导频估计的极大似然(Maximum likelihood,ML)算法。Due to the high dimension of the fading parameters for massive multiple-input multiple-output(MIMO)channel,the computation burden of optimal estimation algorithms is heavy along with the large number of pilots which affects spectral efficiency.To reduce the computational complexity and pilot overhead,two improved semi-blind iterative algorithms based on expectation maximization(EM)estimation are proposed.A small number of orthogonal pilot sequences are used to estimate the initial values.Users are clustered into several groups by exploiting the large-scale fading coefficients between users and base station.Thus,the received noise is proportionally assigned according to these coefficients.The mean and variance of channel parameters are then derived using the statistical characteristics of data.Simulation results show that the mean square error(MSE)of the algorithm is better than that of the maximum likelihood(ML)algorithm when the number of pilots is far less than the estimated parameters.

关 键 词：5G 大规模多输入多输出 半盲信道估计 迭代算法 噪声分配 

分 类 号：TN929.5[电子电信—通信与信息系统]