文献类型：期刊文献

中文题名：一种组稀疏信道估计中的信号重构优化方法

英文题名：A Signal Reconstruction Optimization Method in Group Sparse Channel Estimation

作者：马恒达[1];袁伟娜[1];徐睿[1]

机构：[1]华东理工大学信息科学与工程学院

年份：2019

卷号：45

期号：4

起止页码：646

中文期刊名：华东理工大学学报（自然科学版）

外文期刊名：Journal of East China University of Science and Technology

收录：CSTPCD;;Scopus;北大核心:【北大核心2017】；CSCD:【CSCD_E2019_2020】；

基金：国家自然科学基金(61501187);中央高校基本科研业务费

语种：中文

中文关键词：OFDM;基扩展模型;压缩感知;稀疏信号;信道估计

外文关键词：OFDM;basis expansion model;compressed sensing;sparse signal;channel estimation

摘要：针对正交频分复用(OFDM)系统信道的稀疏性,同时考虑信道的时间选择性和频率选择性,运用压缩感知理论研究了基于组稀疏压缩感知(GSCS)的时变信道估计方法.该方法通过信道系数和基扩展系数的稀疏表示,提出了组稀疏概念以测量、重构信号.在GSCS信号重构过程中,提出了一种新的优化方法,引入一个纠正过程,剔除错误的原子,提高了组稀疏估计方法的信号重构性能.分别在单天线和多天线系统中进行仿真实验,结果验证了本文方法的优越性.
In this paper, the compressed sensing theory is used to estimate the channel according to the sparseness of OFDM system channel. Meanwhile, the time selectivity and frequency selectivity of the channel are investigated and a general channel coefficient base spreading model is given. In many practical scenarios, the non-zero components of sparse signals tend to appear in clusters and some more general forms of structured sparsity naturally, e.g., when dealing with the multi-band signals in the measurements of gene expression levels, or in magnetoencephalography. In order to exploit this structure to improve the reconstruction quality, this paper introduces the methodology of group sparse compressed sensing (GSCS). Moreover, by utilizing the sparse representation of channel coefficients and combining the intrinsic group sparse characteristics of the channel, this paper presents an improved optimization method based on GSCS to improve the sparse estimation. This method can correct the wrong atom with a corrective process such that the signal reconstruction performance of the group sparse estimation method can be improved. Finally, the proposed method is applied to a MIMO system, whose simulation results show that it has a certain performance improvement.