MIMO-OFDM 系统基于压缩感知的稀疏信道估计

doi:10.3969/j.issn.0255-8297.2013.03.005

应用科学学报 ›› 2013, Vol. 31 ›› Issue (3): 245-251.doi: 10.3969/j.issn.0255-8297.2013.03.005

MIMO-OFDM 系统基于压缩感知的稀疏信道估计

叶新荣1,2，朱卫平1，孟庆民1

1. 南京邮电大学信号处理与传输研究院，南京210003
2. 安徽师范大学物理与电子信息学院，安徽芜湖241000

收稿日期:2012-09-05 修回日期:2013-02-27 出版日期:2013-05-28 发布日期:2013-02-27
作者简介:叶新荣，博士生，研究方向：通信中的稀疏信号处理，E-mail: aqzhangye@gmail.com；朱卫平，教授，博导，研究方向：通信信号处理，E-mail: zwp@njupt.edu.cn
基金资助:
国家自然科学基金(No.60972041)；高等学校省级优秀青年人才基金(No.2010SQRL030)；江苏省普通高校研究生科研创新计划
项目基金(No.CXZZ11_0397)资助

Sparse Channel Estimation Based on Compressed Sensing for MIMO-OFDM Systems

YE Xin-rong1,2, ZHU Wei-ping1, MENG Qing-min1

1. Institute of Signal Processing and Transmission, Nanjing University of Posts and
Telecommunications, Nanjing 210003, China
2. College of Physics and Electronic Information, Anhui
Normal University, Wuhu 241000, Anhui Province, China

Received:2012-09-05 Revised:2013-02-27 Online:2013-05-28 Published:2013-02-27

摘要/Abstract

摘要： 为了提高MIMO-OFDM系统稀疏信道估计的准确度及减少导频子载波的数目，利用频率选择性衰落信道的冲激响应在时域具有稀疏性的先验信息，将MIMO-OFDM系统的信道估计建模为压缩感知框架里受到噪声干扰的复数信号重构，提出了分别基于稀疏度自适应匹配追踪(sparsity adaptive matching pursuit, SAMP)和
变量分离近似稀疏重构(sparse reconstruction by separable approximation, SRSA)的两种MIMO-OFDM系统稀疏信道估计方法. 仿真结果表明：与传统最小二乘法的信道估计相比，在相同信噪比条件下能获得相等的估计性能，且这两种方法不必将信道的稀疏度作为先验知识，也能减少40%的导频子载波. 在估计准确度方面，基于SAMP的方法优于采用SRSA的方法，前者的MSE和BER性能更接近Cramer-Rao界；但在算法参数设置方面，后者比前者在实际应用中更容易准确设定.

关键词: 稀疏信道估计, 自适应匹配追踪, 变量分离稀疏重构, 多输入多输出, 正交频分复用

Abstract: To improve accuracy of sparse channel estimation and reduce the pilot number in MIMO-OFDM systems, we use the sparse prior information of the channel impulse response in the time domain, and model the estimation of frequency selective fading channel for MIMO-OFDM systems as the reconstruction of complex sparse signal interfered by noise in compressed sensing. Two methods of sparse channel estimation in MIMOOFDM systems are proposed, based on sparsity adaptive matching pursuit (SAMP) and sparse reconstruction by separable approximation (SRSA), respectively. Simulation shows that, under the same signal-to-noise ratio and for the same performance of MSE and BER without prior information of the sparsity, the two proposed methods can reduce pilot signals by 40% as compared to the conventional least square method. In the two methods, the one based on SAMP runs faster and is closer to the Cramer-Rao bound, while parameters of the one based on SRSA are easier to be set in practical applications.

Key words: sparse channel estimation, sparsity adaptive matching pursuit (SAMP), sparse reconstruction by separable approximation (SRSA), multiple input multiple output (MIMO), orthogonal frequency division multiplexing (OFDM)

中图分类号:

TN911.23

叶新荣1,2，朱卫平1，孟庆民1. MIMO-OFDM 系统基于压缩感知的稀疏信道估计[J]. 应用科学学报, 2013, 31(3): 245-251.

YE Xin-rong1,2, ZHU Wei-ping1, MENG Qing-min1. Sparse Channel Estimation Based on Compressed Sensing for MIMO-OFDM Systems[J]. Journal of Applied Sciences, 2013, 31(3): 245-251.

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MIMO-OFDM 系统基于压缩感知的稀疏信道估计

Sparse Channel Estimation Based on Compressed Sensing for MIMO-OFDM Systems

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