Fluid antenna system (FAS) revolutionizes wireless communications via utilizing position-flexible antennas that dynamically optimize channel conditions and mitigate multipath fading. This innovation is particularly valuable in indoor environments, in which signal propagation is severely degraded due to structural obstructions and complex multipath reflections. In this paper, we investigate the channel modeling and the joint optimization of antenna positioning, beamforming, and power allocation for indoor FAS. In particular, we propose a layout-specific channel model, and employ the novel group relative policy optimization (GRPO) algorithm for tackling the optimization problem. Compared to the state-of-the-art Sionna model, our model achieves an 83.3% reduction in computation time with an approximately 3 dB increase in root-mean-square error (RMSE). When simplified to a two-ray model, our model allows for a closed-form antenna position solution with near-optimal performance. For the joint optimization problem, our GRPO algorithm outperforms proximal policy optimization (PPO) and other baselines in sum-rate, while requiring only 50.8% computational resources of PPO, thanks to its group advantage estimation. Simulation results show that increasing either the group size or trajectory length in GRPO does not yield significant improvements in sum-rate, suggesting that these parameters can be selected conservatively without sacrificing performance.

翻译：流体天线系统（FAS）通过利用位置灵活的天线动态优化信道条件并抑制多径衰落，为无线通信带来革命性变革。这项创新在室内环境中尤其具有价值，因为室内信号传播会因结构障碍和复杂的多径反射而严重劣化。本文研究了室内FAS的信道建模以及天线位置、波束成形和功率分配的联合优化问题。具体而言，我们提出了一种布局特定的信道模型，并采用新颖的群体相对策略优化（GRPO）算法来解决该优化问题。与最先进的Sionna模型相比，我们的模型在计算时间上减少了83.3%，同时均方根误差（RMSE）仅增加约3 dB。当简化为双射线模型时，我们的模型能够推导出具有接近最优性能的天线位置闭式解。对于联合优化问题，得益于其群体优势估计机制，我们的GRPO算法在总速率方面优于近端策略优化（PPO）及其他基线方法，且仅需PPO计算资源的50.8%。仿真结果表明，在GRPO中增大群体规模或轨迹长度均不会显著提升总速率，这意味着这些参数可在不牺牲性能的前提下保守选取。