AI 中文总结
研究用于多用户下行链路网络的FSIM，通过联合优化元原子位置、波束成形和相移来灵活配置信道，解决和速率最大化的非凸非线性问题，开发AO算法求解，仿真表明其性能优于传统SIM及现有超表面，算法速率性能也更优。
AI 中文摘要
研究了一种用于多输入单输出（MISO）通信系统的流体元件（FE）辅助堆叠智能超表面（FSIM），其中多天线基站（BS）通过FSIM为多个单天线用户服务。与传统固定元原子部署的SIM不同，该架构允许每层中的元原子在预定义的流体区域内移动以增加空间分集。通过联合优化二维元原子位置、BS发射波束成形和FSIM相移，可灵活重新配置级联的BS-FSIM-用户信道。提出的和速率最大化问题因元件位置、波束成形和相移的耦合解而高度非凸且非线性，为此开发了交替优化（AO）算法迭代更新变量。波束成形和FSIM相移子问题转化为半定规划问题求解，FE位置子问题通过基于投影梯度的更新处理。仿真结果表明，层间厚度小的紧凑型FSIM更优，增加厚度会削弱层间耦合并降低可实现的和速率。结果还表明，所提出的FSIM明显优于具有固定位置、基于贴片的结构、部分流动性、受限流体区域的传统SIM以及现有的灵活智能超表面。此外，所提出的基于AO的算法与子方案、元启发式方法和传统波束成形基准相比，实现了更高的速率性能。
英文摘要
A fluid element (FE)-aided stacked intelligent metasurface (FSIM) for multiple-input single-output (MISO) communication system is investigated, where a multi-antenna base station (BS) serves multiple single-antenna users through FSIM. Unlike conventional SIM with fixed meta-atom deployment, the architecture allows the meta-atoms in each layer to move within a predefined fluidic region to further increase the spatial diversity. By jointly optimizing the two-dimensional meta-atom positions, BS transmit beamforming, and FSIM phase-shifts, the cascaded BS-FSIM-user channels can be flexibly reconfigured to enhance the desired signals and suppress multiuser interference. The proposed sum-rate maximization problem is highly non-convex and nonlinear due to the coupled solutions of element position, beamforming, and phase-shift. To address this challenge, an alternating optimization (AO) algorithm is developed to iteratively update these variables. The beamforming and FSIM phase-shift subproblems are transformed into semi-definite programming problems and solved by using successive convex approximation (SCA), first-order Taylor approximation, and penalty-based rank-one relaxation, whilst the FE position subproblem is handled through a projected gradient-based update. Simulation results reveal that a compact FSIM with a small inter-layer thickness is preferable, as increasing the thickness weakens inter-layer coupling and degrades the achievable sum rate. Results also demonstrate that the proposed FSIM significantly outperforms conventional SIMs with fixed positions, patch-based structures, partial fluidity, restricted fluid regions, and existing flexible intelligent metasurfaces. Furthermore, the proposed AO-based algorithm achieves superior rate performance compared to sub-schemes, metaheuristic methods, and conventional beamforming benchmarks.
