一种基于压缩感知的时变稀疏信道估计方法  

A Novel Time-Varying and Sparse Channel Estimation Based on Compress Sensing

作　　者：马时雨 袁伟娜[1] 王建玲[1] 

机构地区：[1]华东理工大学信息科学与工程学院,上海200237

出　　处：《华东理工大学学报（自然科学版）》2016年第3期382-386,共5页Journal of East China University of Science and Technology

基　　金：国家自然科学基金(61501187)

摘　　要：对于快时变且稀疏环境下的正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)系统模型,现有的方法是基于基扩展模型(Basic Expansion Model,BEM)进行估计,并利用恒定幅值零自相关(Constant Amplitude Zero Auto Correlation,CAZAC)序列估计时延。本文利用信道响应中稀疏的观测矩阵,用压缩感知(Compress Sensing,CS)的正交匹配跟踪(Orthogonal Matching Pursuit,OMP)算法进行时延估计。仿真结果表明,两种方法都能对时延进行有效的筛选,但当多普勒频移增大、信噪比较低时,本文将OMP、BEM相结合的方法效果较优。In fast-varying and sparse orthogonal frequency division multiplexing （OFDM） system model, the existing methods utilize basic expansion model to estimation and use constant amplitude zero auto correlation （CAZAC） sequence to detect delays. By means of the channel＇s sparse response matrix, this paper proposes a compress sensing（CS） method for detecting delays via orthogonal matching pursuit （OMP）. Simulation results show that both CAZAC and OMP methods can improve the effectiveness of channel estimation. However, when Doppler shift is increasing, the proposed method can attain better performance.

关 键 词：信道估计 基扩展模型 稀疏 CAZAC序列 压缩感知 

分 类 号：TN929.5[电子电信—通信与信息系统]