Deep Learning-Based Precoder Design for Network Massive MIMO Transmission

We investigate the linear precoding for sum-rate maximization in network massive multiple-input multiple-output (MIMO) transmission, where the cooperative transmission by all base stations (BSs) enhances the capacity, reliability, and robustness. To address the growing complexity of traditional iter...

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Bibliographic Details
Published in:IEEE transactions on wireless communications p. 1
Main Authors: Zhu, Wen-Jie, Sun, Chen, Gao, Xiqi, Xia, Xiang-Gen
Format: Journal Article
Language:English
Published: IEEE 2025
Subjects:
Complexity theory
convolutional neural network
Deep learning
Iterative algorithms
Massive MIMO
Network massive MIMO transmission
Optimization
Precoding
Receivers
Symmetric matrices
Training
Vectors
WMMSE precoder
ISSN:1536-1276, 1558-2248
Online Access:Get full text
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Summary:We investigate the linear precoding for sum-rate maximization in network massive multiple-input multiple-output (MIMO) transmission, where the cooperative transmission by all base stations (BSs) enhances the capacity, reliability, and robustness. To address the growing complexity of traditional iterative algorithms in large-scale systems, we leverage the weighted minimum mean square error (WMMSE) solution and show that the precoding vectors can be fully reconstructed from a set of low-dimensional parameters. By exploiting the structure and relationship of these parameters, we reformulate the original problem in a reduced-dimensional space while preserving equivalence to the original solution. Deep learning techniques are employed to solve this reformulated problem, where equivalent scaling of the variables facilitates pre-processing for training and further reduces the dimension of the learning input. A neural network is trained on the refined low-dimensional objectives with a tailored loss, allowing the precoding vectors to be directly calculated from its output. As demonstrated by numerical results, the proposed deep learning-based precoder performs well with considerably reduced online processing complexity.
ISSN:1536-1276
1558-2248
DOI:10.1109/TWC.2025.3597408