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

Abstract

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)

CLC Number:

TN929.5

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.

Figures/Tables 11

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References 19

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算法类型	计算复杂度
LMMSE	O(M(2N³+N²))
DCS-LMMSE	Ο(S_nN(T-S)(M-S)+NS+r²MN)

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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Distributed compressed sensing LMMSE channel estimation in massive MIMO systems

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