Joint Beamforming and Antenna Position Optimization for Fluid Antenna-Assisted MU-MIMO Networks

The fluid antenna system (FAS) is a disruptive technology for future wireless communication networks. This paper considers the joint optimization of beamforming matrices and antenna positions for weighted sum rate (WSR) maximization in fluid antenna (FA)-assisted multiuser multiple-input multiple-ou...

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Vydáno v:IEEE journal on selected areas in communications s. 1
Hlavní autoři: Liao, Tianyi, Guo, Wei, He, Hengtao, Song, Shenghui, Zhang, Jun, Letaief, Khaled B.
Médium: Journal Article
Jazyk:angličtina
Vydáno: IEEE 2025
Témata:
Array signal processing
decentralized baseband processing (DBP)
Fading channels
Fluid antenna system (FAS)
Fluids
fractional programming (FP)
majorization maximization (MM)
MIMO
MU-MIMO
Optimization
Radio frequency
Symmetric matrices
Three-dimensional displays
Transforms
Vectors
ISSN:0733-8716, 1558-0008
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Shrnutí:The fluid antenna system (FAS) is a disruptive technology for future wireless communication networks. This paper considers the joint optimization of beamforming matrices and antenna positions for weighted sum rate (WSR) maximization in fluid antenna (FA)-assisted multiuser multiple-input multiple-output (MU-MIMO) networks, which presents significant challenges due to the strong coupling between beamforming and FA positions, the non-concavity of the WSR objective function, and high computational complexity. To address these challenges, we first propose a novel block coordinate ascent (BCA)-based method that employs matrix fractional programming techniques to reformulate the original complex problem into a more tractable form. Then, we develop a parallel majorization maximization (MM) algorithm capable of optimizing all FA positions simultaneously. To further reduce computational costs, we propose a decentralized implementation based on the decentralized base-band processing (DBP) architecture. Simulation results demonstrate that our proposed algorithm not only achieves significant WSR improvements over conventional MIMO networks but also outperforms the existing method. Moreover, the decentralized implementation substantially reduces computation time while maintaining similar performance compared with the centralized implementation.
ISSN:0733-8716
1558-0008
DOI:10.1109/JSAC.2025.3615569