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Sub-Array Hybrid Precoding for Massive MIMO Systems: A CNN-Based Approach
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DOI：10.1109/LCOMM.2020.3022898.png)
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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.
Keyword:
Hybrid precoding
sub-array architecture
massive MIMO
CNN
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4.4
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1.3W
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2.2W
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