大规模MIMO系统中分布式压缩感知LMMSE信道估计

doi:10.12305/j.issn.1001-506X.2021.03.28

摘要/Abstract

摘要：

大规模多输入多输出(multiple input multiple output, MIMO)系统中，信道估计算法复杂度随着基站侧天线数量的增加而急剧增加, 针对需要在信道估计算法复杂度与算法性能之间进行折中的问题，提出分布式压缩感知线性最小均方误差(distributed compressed sensing linear minimum mean square error, DCS-LMMSE)算法。该算法利用信道的空时共稀疏性, 首先根据先验支撑集信息将接收信号分为密集部分和稀疏部分, 然后分别采用不同的算法进行初始信道估计, 最后采用奇异值分解代替信道相关矩阵求逆进一步降低DCS-LMMSE算法复杂度。所提算法与传统线性最小均方误差算法相比明显地降低了计算复杂度。仿真结果表明, 所提算法与纯压缩感知稀疏信道估计算法相比具有更好的性能。

关键词: 大规模多输入多输出, 空时共稀疏性, 信道估计, 分布式压缩感知线性最小均方误差

Abstract:

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.

Key words: massive multiple input multiple output (MIMO), space-time co-sparseness, channel estimation, distributed compressed sensing linear minimum mean square error (DCS-LMMSE)

中图分类号:

TN929.5

李贵勇, 于敏, 余永坤. 大规模MIMO系统中分布式压缩感知LMMSE信道估计[J]. 系统工程与电子技术, 2021, 43(3): 823-831.

Guiyong LI, Min YU, Yongkun YU. Distributed compressed sensing LMMSE channel estimation in massive MIMO systems[J]. Systems Engineering and Electronics, 2021, 43(3): 823-831.

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参考文献 19

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仿真参数	设定值
BS侧天线数	128
小区内用户个数	30
传输信噪比/dB	[0:5:25]
角度域稀疏系数	8
OFDM子载波数	1 024
导频数	256
OFDM符号数	56
调制阶数	16QAM
UE与BS之间距离/m	50

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