大规模MIMO系统稀疏度自适应信道估计算法

doi:10.11959/j.issn.1000-436x.2017291

Abstract

Abstract:

A sparsity-adaptive channel estimation algorithm based on compressive sensing was proposed for massive MIMO systems when the number of channel multi-paths was unknown.By exploiting the joint sparsity characteristics of the sub-channels，the proposed block sparsity adaptive matching pursuit (BSAMP) algorithm first selected atoms by setting a threshold and finding the position of the maximum backward difference，which reduces the energy dispersion caused by the non-orthogonality of the observation matrix and improves the performance of the algorithm.Then a regularization method was utilized to improve the stability of the algorithm.Simulation results demonstrate that the proposed algorithm recovers the channel state information accurately and shows a high computational efficiency.

Key words: massive MIMO, compressive sensing, channel estimation, sparsity adaptive

CLC Number:

TN914.4

Li-jun GE,Hui GUO,Yue LI,Lan ZHAO. Sparsity adaptive channel estimation algorithm based on compressive sensing for massive MIMO systems[J]. Journal on Communications, 2017, 38(12): 57-62.

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References 17

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稀疏信道恢复算法	平均运行时间/s
LS算法	0.023 28
OMP算法	2.288 70
SP算法	1.106 90
SAMP算法	93.393 0
ASSP算法	15.361 0
BSAMP算法	0.012 84

Sparsity adaptive channel estimation algorithm based on compressive sensing for massive MIMO systems

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