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应用科学学报 >

2011 , Vol. 29 >Issue 4: 347 - 352

DOI: https://doi.org/10.3969/j.issn.0255-8297.2011.04.003

通信工程

基于压缩感知的MIMO系统稀疏信道估计

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  • 1. 北京邮电大学信息与通信工程学院，北京100876
    2. 电子科技大学电子工程学院，成都611731
    3. 东北大学工学部电子与通信工程系，日本仙台980-8579
王妮娜，博士生，研究方向：压缩感知、稀疏多径信道估计，E-mail: ninawnn@gmail.com

收稿日期: 2010-12-20

  修回日期: 2011-05-16

  网络出版日期: 2011-07-30

基金资助

国家科技重大专项基金(No.2009ZX03002-009-01)资助

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Compressed Sensing Based Sparse Channel Estimation in MIMO Systems

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  • 1. School of Information and Communication Engineering, Beijing University of Posts and Telecommunications,
    Beijing 100876, China
    2. School of Electronic and Engineering, University of Electronic Science and Technology of China,
    Chengdu 611731, China
    3. Department of Electrical and Communication Engineering, Graduate School of Engineering,
    Tohoku University, Sendai 980-8579, Japan

Received date: 2010-12-20

  Revised date: 2011-05-16

  Online published: 2011-07-30

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摘要

在多输入多输出通信系统中，接收端信道均衡与相干检测需要利用信道状态信息. 传统信道估计方法如最小二乘法和最小均方误差法均基于多径信道密集型假设，导致频谱利用率低下. 为此，该文研究在单载波MIMO系统中的稀疏信道估计，利用多径信道的稀疏特性提出一种基于压缩感知理论的信道估计方法. 这种方法能利用较少的导频信号达到与传统方法相比拟的估计性能，从而提高频谱利用率. 仿真验证和理论分析表明，基于压缩采样匹配追踪的压缩感知信道估计方法为MIMO系统稀疏信道估计的最优选择.

关键词： 压缩感知; 稀疏多径; 信道估计; CoSaMP算法; 多输入多输出

本文引用格式

王妮娜1， 桂冠2;3， 张治1， 唐恬1 . 基于压缩感知的MIMO系统稀疏信道估计[J]. 应用科学学报, 2011 , 29(4) : 347 -352 . DOI: 10.3969/j.issn.0255-8297.2011.04.003

Abstract

Abstract: In MIMO systems, channel state information (CSI) is necessary for coherent detection and channel
equalization at the receiver. Traditional channel estimation methods such as least squares (LS) and minimum
mean square error (MMSE) are based on the rich multipath assumption which leads to low frequency spectrum
utilization. This paper studies sparse channel estimation for single carrier MIMO systems. A novel method
based on compressed sensing is proposed by using sparsity, which can obtain accurate CSI with fewer pilots so
as to improve frequency spectrum utilization. Simulation and theoretical analysis show that the compressive
sampling matching pursuit (CoSaMP) estimation is the best choice for MIMO sparse multipath channel estimation.

Key words： compressed sensing (CS), sparse multipath, channel estimation, CoSAMP algorithm, multipleinput multiple-output (MIMO)

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