Distributed compressed sensing LMMSE channel estimation in massive MIMO systems

被引：0
|
作者：
Li G. [1 ]
Yu M. [1 ]
Yu Y. [1 ]
机构：
[1] School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2021年 / 43卷 / 03期
关键词：
Channel estimation; Distributed compressed sensing linear minimum mean square error (DCS-LMMSE); Massive multiple input multiple output (MIMO); Space-time co-sparseness;
D O I：
10.12305/j.issn.1001-506X.2021.03.28
中图分类号：
学科分类号：
摘要：
In massive multiple input multiple output (MIMO) systems, the complexity of channel estimation algorithm increases rapidly with the increase of the number of antennas on the base station side. To solve the problem of tradeoff between the complexity of channel estimation algorithm and the performance of the algorithm, a distributed compressed sensing linear minimum mean square error (DCS-LMMSE) algorithm is proposed. The algorithm takes advantage of the space-time co-sparseness of the channel. Firstly, the received signals are divided into dense part and sparse part according to the prior support set information. Then, the different algorithms are used to estimate the initial channel. Finally, singular value decomposition is used to replace the inverse of channel correlation matrix to further reduce the complexity of DCS-LMMSE algorithm. Compared with the traditional LMMSE algorithm, the proposed algorithm significantly reduces the computational complexity. Simulation results show that the proposed algorithm has better performance than the pure compressed sensing sparse channel estimation algorithm. © 2021, Editorial Office of Systems Engineering and Electronics. All right reserved.
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页码：823 / 831
页数：8
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