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.

翻译：流体天线系统（FAS）作为一种颠覆性技术，通过根据传播环境变化动态配置天线，为未来无线网络提供了前所未有的自由度（DoF）。将流体天线（FA）与多用户多输入多输出（MU-MIMO）网络相结合，有望通过联合波束成形与FA位置优化实现显著的加权和速率（WSR）增益。然而，由于波束成形矩阵与天线位置之间的强耦合性，联合设计具有挑战性。为解决这一难题，我们在FA辅助的MU-MIMO网络中提出了一种基于块坐标上升（BCA）的新方法。具体而言，我们首先采用矩阵分式规划技术将原始复杂问题重构为更易处理的形式。随后，遵循BCA原理求解重构后的问题，其中我们开发了一种能够同时优化所有FA位置的低复杂度最大化最小化算法。为进一步降低计算、存储与互连成本，我们通过利用分布式基带处理（DBP）架构，为所提算法提出了一种分布式实现方案。仿真结果表明，采用所提算法后，FA辅助的MU-MIMO系统相比配备固定位置天线的传统MIMO网络，可实现高达47%的WSR提升。此外，分布式实现方案相比集中式实现，在保持相近性能的同时，计算时间减少了约70%。