AI 中文总结
针对CSI不完美的FARIS辅助多用户系统，提出联合优化基站波束成形器、FARIS系数及主动单元选择的鲁棒传输方案，通过WMMSE与AO框架求解，性能优于传统设计。
AI 中文摘要
本文针对流体型可重构智能表面（FARIS）辅助的多用户系统，在信道状态信息（CSI）不完美的情况下，提出一种鲁棒传输设计方案，该方案支持主动反射与动态端口选择，可提供更高灵活性。我们通过联合优化基站波束成形器、FARIS系数及主动单元选择，同时明确考虑CSI误差与实际功率约束，构建鲁棒最小和速率最大化问题。由于优化变量耦合及离散端口选择结构，该问题本质上是非凸的。为解决此挑战，我们首先通过加权最小均方误差（WMMSE）方法重构原问题，再设计交替优化（AO）框架，其中每个子问题可高效求解，且整体算法收敛至驻点。仿真结果表明，所提鲁棒FARIS方案始终优于传统设计，凸显了在CSI不确定性下联合利用自由度（DoF）增强与主动信号放大的有效性。
英文摘要
In this paper, we propose a robust transmission design for multi-user systems assisted by a fluid active reconfigurable intelligent surface (FARIS), which enables both active reflection and dynamic port selection and thereby offers enhanced flexibility, under imperfect channel state information (CSI). We formulate a robust minimum sum-rate maximization problem by jointly optimizing the base station beamformer, the utilized FARIS coefficients, and the active element selection, while explicitly accounting for CSI errors and practical power constraints. The resulting problem is inherently nonconvex due to the coupled optimization variables and discrete port-selection structure. To tackle this challenge, we first reformulate the original problem via a weighted minimum mean square error (WMMSE) approach and then devise an alternating optimization (AO) framework, where each resulting subproblem admits efficient solutions and the overall algorithm converges to a stationary point. Simulation results demonstrate that the proposed robust FARIS scheme consistently outperforms conventional designs, highlighting the effectiveness of jointly leveraging degree-of-freedom (DoF) enhancement and active signal amplification under CSI uncertainty.
