文献类型：期刊文献

中文题名：一种新的基于分布式压缩感知的时变稀疏信道估计

英文题名：A Novel Time-Varying and Sparse Channel Estimation Based on Distributed Compress Sensing

作者：邵耀东[1];袁伟娜[1];王嘉璇[1]

机构：[1]华东理工大学信息科学与工程学院,上海200237

年份：2018

卷号：44

期号：1

起止页码：119

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_E2017_2018】；

基金：国家自然科学基金(61501187)

语种：中文

中文关键词：信道估计;CE-BEM;分布式压缩感知;导频放置;稀疏度自适应

外文关键词：channel estimation; CE-BEM; distributed compressive sensing; pilot design; sparsity adaptive

摘要：对OFDM(Orthogonal Frequency Division Multiplexing)系统中快时变稀疏信道估计进行了研究,采用CE-BEM模型(Complex Exponential-Basis Expansion Model)对时变信道进行建模。由于信道是稀疏的,所以对应的CE-BEM基的系数也是稀疏的,由此将估计信道的抽头响应问题转化为求解稀疏CE-BEM系数的问题。针对现实稀疏度未知的场景,提出了一种稀疏度自适应的分布式压缩感知(Distributed Compressive Sensing,DCS)算法。考虑到导频放置对性能影响的重要性,提出了一种新的放置方式。仿真结果表明,本文算法有效地提升了估计性能。
This paper studies the problem of time-varying and sparse channel estimation in the orthogonal frequency division multiplexing （OFDM） system. The complex exponential-basis expansion model （CE-BEM） is used to model the time-varying channel＇s response. The coefficient in CE-BEM is sparse due to the channel’s sparsity. Thus, the problem of estimating the sparse channel response turns into solving the coefficient of sparse CE-BEM. For the scene with unknown sparsity, this paper proposes a sparsity adaptive distributed compressive sensing algorithm. Furthermore, a pilot pattern design method is given to deal with the effect of pilot placement on channel estimation. Finally, the simulation results show that the proposed algorithm can effectively improve estimating performance.