Model-Driven Deep Learning for Hybrid Precoding in Millimeter Wave MU-MIMO System

The use of a hybrid analog-digital architecture that connects one RF chain to multiple antennas through phase shifters is an energy-efficient solution for multiuser multiple-input multiple-output (MU-MIMO) systems. However, designing the hybrid precoder is challenging due to its nonconvex objective...

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Bibliographic Details
Published in:IEEE transactions on communications Vol. 71; no. 10; p. 1
Main Authors: Jin, Weijie, Zhang, Jing, Wen, Chao-Kai, Jin, Shi
Format: Journal Article
Language:English
Published: New York IEEE 01.10.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Antennas
Changing environments
Computational complexity
Deep learning
hybrid precoding
Hybrid systems
manifold optimization
Millimeter wave communication
Millimeter waves
MIMO communication
model-driven deep learning
MU-MIMO
Neural networks
Optimization
Performance enhancement
Phase shifters
Precoding
Radio frequency
Robustness
WMMSE
ISSN:0090-6778, 1558-0857
Online Access:Get full text
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Summary:The use of a hybrid analog-digital architecture that connects one RF chain to multiple antennas through phase shifters is an energy-efficient solution for multiuser multiple-input multiple-output (MU-MIMO) systems. However, designing the hybrid precoder is challenging due to its nonconvex objective functions and constraints. Existing algorithms struggle with high computational complexity or poor performance, which often result from slow or no convergence. This study proposes a solution that leverages model-driven deep learning (DL) to maximize the spectral efficiency of MU-MIMO systems through hybrid precoding. The optimization problem is first transformed into a weighted minimum mean square error optimization. Then, it is combined with manifold optimization and DL to improve performance and simplify the process. The algorithm is designed to be robust in changing environments and utilizes DL to address imperfect channel state information. Simulation results show that the proposed method outperforms existing algorithms, is robust in changing system parameters, and can even outperforms fully digital precoding with the same number of antennas.
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ISSN:0090-6778
1558-0857
DOI:10.1109/TCOMM.2023.3296622