Efficient MU-MIMO Beamforming Based on Majorization-Minimization and Deep Unfolding

To release the full potentials of massive multi-user multiple-input multiple-output (MU-MIMO) for wireless communication, beamforming design is a must. In this paper, three algorithms are progressively developed for the maximization of the weighted sum-rate (WSR) problem. First, an effective beamfor...

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Vydané v:IEEE transactions on wireless communications Ročník 24; číslo 5; s. 3949 - 3963
Hlavní autori: Xu, Qian, Sun, Jianyong, Xu, Zongben
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.05.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Approximation algorithms
Array signal processing
Beamforming
Complexity
Convergence
deep unfolding
Downlink
Heuristic algorithms
Interference
majorization-minimization
massive MIMO
MIMO communication
Optimization
Signal to noise ratio
Streams
Wireless communication
Wireless communications
ISSN:1536-1276, 1558-2248
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Shrnutí:To release the full potentials of massive multi-user multiple-input multiple-output (MU-MIMO) for wireless communication, beamforming design is a must. In this paper, three algorithms are progressively developed for the maximization of the weighted sum-rate (WSR) problem. First, an effective beamforming algorithm is developed by applying the majorization-minimization (MM) procedure in two stages, by which the WSR problem is transformed into a series of quadratic convex problems. We prove that the proposed algorithm converges to a stationary point of the WSR. Second, to improve the efficiency of the two-stage beamforming algorithm, the inherent low-dimensional structure within the beamforming update is exploited aiming to reduce the computational complexity of the matrix inversion. Third, to further reduce the complexity, a deep unfolding beamforming network is developed, which unfolds the improved beamforming algorithm into a layer-wise structure and employs a trainable module structured on the dynamics developed to approximate the matrix inversion. Experimental results demonstrate that the proposed algorithms perform significantly better than the classical weighted minimum mean square error (WMMSE) beamforming and state-of-the-art deep unfolding beamformers in terms of sum-rate and require significantly less CPU time.
Bibliografia:ObjectType-Article-1
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content type line 14
ISSN:1536-1276
1558-2248
DOI:10.1109/TWC.2025.3541144