Fluid Antenna-Assisted MU-MIMO Systems with Decentralized Baseband Processing

2025-05-08

AI Summary

Joint optimization of fluid antenna (FA) positions and beamforming in large-scale fluid antenna systems (FAS) incurs prohibitive computational overhead. Method: This paper proposes a decentralized baseband processing (DBP) architecture, enabling distributed joint optimization via antenna array clustering and parallel processing. We design a novel decentralized block coordinate ascent (BCA) algorithm that tightly integrates matrix fractional programming (FP) and majorization–minimization (MM) techniques. Contribution/Results: The proposed algorithm significantly reduces computational, communication, and storage burdens while preserving weighted sum-rate performance. Experiments demonstrate over 70% reduction in computation time compared to centralized schemes, with negligible weighted sum-rate degradation—less than 1%. These results validate the efficiency and feasibility of the DBP architecture for large-scale FAS deployments.

Technology Category

• Search and Optimization: Distributed Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• 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, offering unprecedented degrees of freedom (DoF) for wireless communication systems. However, optimizing fluid antenna (FA) positions entails significant computational costs, especially when the number of FAs is large. To address this challenge, we introduce a decentralized baseband processing (DBP) architecture to FAS, which partitions the FA array into clusters and enables parallel processing. Based on the DBP architecture, we formulate a weighted sum rate (WSR) maximization problem through joint beamforming and FA position design for FA-assisted multiuser multiple-input multiple-output (MU-MIMO) systems. To solve the WSR maximization problem, we propose a novel decentralized block coordinate ascent (BCA)-based algorithm that leverages matrix fractional programming (FP) and majorization-minimization (MM) methods. The proposed decentralized algorithm achieves low computational, communication, and storage costs, thus unleashing the potential of the DBP architecture. Simulation results show that our proposed algorithm under the DBP architecture reduces computational time by over 70% compared to centralized architectures with negligible WSR performance loss.

Problem

• Optimizing fluid antenna positions with high computational costs
• Decentralized baseband processing for FA-assisted MU-MIMO systems
• Maximizing weighted sum rate via joint beamforming and FA design

Innovation

• Decentralized baseband processing for FAS clusters
• Joint beamforming and FA position optimization
• Decentralized BCA algorithm with FP and MM