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Sub-Array Hybrid Precoding for Massive MIMO Systems: A CNN-Based Approach

delete2021-01-01
delete18
PRE
AI
K
Kai Chen
J
Jing Yang
Q
Qiang Li
X
Xiaohu Ge *
DOI：10.1109/LCOMM.2020.3022898delete
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Abstract

Abstract

En 中文
In order to reduce the computation time of hybrid precoding processing while improving the spectrum efficiency (SE) of massive multiple-input multiple-output (MIMO) systems, in this letter, we investigate the sub-array hybrid precoding based on the convolutional neural network (CNN). A constraint-relaxation alternating minimization (CR Alt-Min) algorithm is proposed to create the training set of the CNN. To reduce the computation time caused by iterations in the Alt-Min algorithm, a CNN-based algorithm is proposed. Simulation results show that the CNN-based algorithm reduces the computation time in hybrid precoding processing by an order of magnitude. Moreover, the maximum SE is improved by 26.64% by the CNN-based algorithm, compared with the Alt-Min algorithm.
Keywords:
Hybrid precoding
sub-array architecture
massive MIMO
CNN
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Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

IEEE Communications Letters cover
IEEE Communications Letters
IF:
4.4
Papers:
1.3W
Citations:
2.2W

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