发表机构
Tongling University; University of Oulu; Mid Sweden University; University of Luxembourg（铜陵大学; 奥卢大学; 瑞典中部大学; 卢森堡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出将可移动单元STARS与RSMA集成于SWIPT系统，通过联合优化波束成形、单元位置等应对CSI不确定性和硬件损伤，实现鲁棒高和速率。
AI 中文摘要
我们研究了一个用于同时无线信息和功率传输（SWIPT）的鲁棒多用户框架，其中速率分割多址（RSMA）与可移动单元同时发射和反射表面（ME-STARS）相结合。通过允许STARS单元在控制反射和透射系数之外改变其位置，所提出的架构提供了额外的空间灵活性，以改善朝向两个区域用户的级联信道。同时，采用RSMA来管理多用户干扰，而功率分割接收机能够同时进行信息解码和能量采集。系统设计进一步考虑了信道状态信息（CSI）的不确定性、残余收发器硬件损伤（HIs）以及实际能量采集电路的非线性特性。据此，通过联合设计基站预编码器、公共速率分配、ME-STARS反射/透射系数、功率分割（PS）比率和可移动单元位置，构建了一个鲁棒的和速率最大化问题。由于可移动单元位置非线性地影响级联信道，并与其余设计变量紧密耦合，所得公式高度非凸。为了获得可处理的解，联合设计被分解为主动波束成形、被动波束成形、可移动单元定位和PS比率优化模块，并通过适当的凸重构迭代更新。数值结果表明，所提出的框架相比所考虑的基准方案提供了更高的和速率，在CSI不确定性和残余HI下表现出鲁棒性，并在所考虑的系统配置中实现了稳定收敛。
英文摘要
We study a robust multiuser framework for simultaneous wireless information and power transfer (SWIPT), where rate-splitting multiple access (RSMA) is integrated with a movable-element simultaneously transmitting and reflecting surface (ME-STARS). By allowing the STARS elements to change their positions in addition to controlling the reflection and transmission coefficients, the proposed architecture provides additional spatial flexibility for improving the cascaded channels toward users in both regions. Meanwhile, RSMA is employed to manage multiuser interference, while power-splitting receivers enable simultaneous information decoding and energy harvesting. The system design further accounts for channel state information (CSI) uncertainty, residual transceiver hardware impairments (HIs), and the nonlinear characteristics of practical energy-harvesting circuits. Accordingly, a robust sum-rate maximization problem is formulated by jointly designing the BS precoders, common-rate allocation, ME-STARS reflection/transmission coefficients, power-splitting (PS) ratios, and movable-element positions. The resulting formulation is highly non-convex because the movable-element positions affect the cascaded channels non-linearly and are tightly coupled with the remaining design variables. To obtain a tractable solution, the joint design is decomposed into active beamforming, passive beamforming, movable-element positioning, and PS-ratio optimization blocks, which are updated iteratively through suitable convex reformulations. Numerical results show that the proposed framework delivers higher sum rates than the considered benchmark schemes, exhibits robust behavior under CSI uncertainty and residual HIs, and achieves stable convergence across the considered system configurations.
Comments14 pages, 9 figures
