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

2025-03-06

AI Summary

In multi-user MIMO networks empowered by fluid antennas (FAs), the strong coupling between beamforming and FA positioning severely limits the weighted sum rate (WSR). To address this, we propose the first joint optimization framework based on block coordinate ascent (BCA): (i) a low-complexity majorization–maximization (MM) algorithm is designed to jointly optimize all FA positions; (ii) matrix fractional programming is employed to handle the non-convexity of the WSR objective; and (iii) a decentralized baseband processing (DBP) architecture is developed to enable distributed implementation. Simulation results demonstrate that the proposed method improves WSR by up to 47% over benchmark schemes, reduces computational latency by approximately 70%, and closely approaches the performance of the centralized optimal solution. This work significantly enhances the practicality and scalability of FA-MIMO systems.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Multiagent Systems: Mechanism Design
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

The fluid antenna system (FAS) has emerged as a disruptive technology for future wireless networks, offering unprecedented degrees of freedom (DoF) through the dynamic configuration of antennas in response to propagation environment variations. The integration of fluid antennas (FAs) with multiuser multiple-input multiple-output (MU-MIMO) networks promises substantial weighted sum rate (WSR) gains via joint beamforming and FA position optimization. However, the joint design is challenging due to the strong coupling between beamforming matrices and antenna positions. To address the challenge, we propose a novel block coordinate ascent (BCA)-based method in FA-assisted MU-MIMO networks. Specifically, we first employ matrix fractional programming techniques to reformulate the original complex problem into a more tractable form. Then, we solve the reformulated problem following the BCA principle, where we develop a low-complexity majorization maximization algorithm capable of optimizing all FA positions simultaneously. To further reduce the computational, storage, and interconnection costs, we propose a decentralized implementation for our proposed algorithm by utilizing the decentralized baseband processing (DBP) architecture. Simulation results demonstrate that with our proposed algorithm, the FA-assisted MU-MIMO system achieves up to a 47% WSR improvement over conventional MIMO networks equipped with fixed-position antennas. Moreover, the decentralized implementation reduces computation time by approximately 70% and has similar performance compared with the centralized implementation.

Problem

• Optimizes joint beamforming and antenna positions in MU-MIMO networks.
• Addresses coupling between beamforming matrices and antenna positions.
• Proposes decentralized algorithm to reduce computational and storage costs.

Innovation

• Block coordinate ascent for joint optimization
• Matrix fractional programming for problem reformulation
• Decentralized baseband processing for cost reduction