A Deep Learning-Based Framework for Low Complexity Multiuser MIMO Precoding Design

Using precoding to suppress multi-user interference is a well-known technique to improve spectra efficiency in multiuser multiple-input multiple-output (MU-MIMO) systems, and the pursuit of high performance and low complexity precoding method has been the focus in the last decade. The traditional al...

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Bibliographic Details
Published in:IEEE transactions on wireless communications Vol. 21; no. 12; pp. 11193 - 11206
Main Authors: Zhang, Maojun, Gao, Jiabao, Zhong, Caijun
Format: Journal Article
Language:English
Published: New York IEEE 01.12.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Artificial neural networks
Complexity
Computational complexity
Deep learning
Downlink
Machine learning
MIMO communication
MIMO-OFDM
MISO communication
model compression
Multi-user MIMO
Orthogonal Frequency Division Multiplexing
precoder design
Precoding
Resource management
Wireless communication
ISSN:1536-1276, 1558-2248
Online Access:Get full text
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Summary:Using precoding to suppress multi-user interference is a well-known technique to improve spectra efficiency in multiuser multiple-input multiple-output (MU-MIMO) systems, and the pursuit of high performance and low complexity precoding method has been the focus in the last decade. The traditional algorithms including the zero-forcing (ZF) algorithm and the weighted minimum mean square error (WMMSE) algorithm failed to achieve a satisfactory trade-off between complexity and performance. In this paper, leveraging on the power of deep learning, we propose a low-complexity precoding design framework for MU-MIMO systems. The key idea is to transform the MIMO precoding problem into the multiple-input single-output precoding problem, where the optimal precoding structure can be obtained in closed-form. A customized deep neural network is designed to fit the mapping from the channels to the precoding matrix. In addition, the technique of input dimensionality reduction, network pruning, and recovery module compression are used to further improve the computational efficiency. Furthermore, the extension to the practical MIMO orthogonal frequency-division multiplexing (MIMO-OFDM) system is studied. Simulation results show that the proposed low-complexity precoding scheme achieves similar performance as the WMMSE algorithm with very low computational complexity.
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ISSN:1536-1276
1558-2248
DOI:10.1109/TWC.2022.3190435