发表机构
Beihang University; State Key Laboratory of CNS/ATM; Queen’s University Belfast; the University of Hong Kong（北京航空航天大学; 空管系统国家实验室; 贝尔法斯特女王大学; 香港大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出可移动元件STARS辅助NOMA系统，通过联合优化波束成形与元件位置，采用交替优化算法提升和速率，仿真验证性能优于固定位置方案。
AI 中文摘要
本文提出了一种可移动元件（ME）启用的同时透射和反射表面（ME-STARS）辅助的非正交多址接入（NOMA）通信系统，其中STARS元件的位置可以灵活重新配置，以引入额外的空间自由度。我们的目标是通过联合优化基站（BS）处的有源波束成形以及STARS处的无源波束成形和ME位置来最大化总和可达速率。为了解决由此产生的高度耦合的非凸优化问题，提出了一种基于交替优化的算法，其中原始问题被分解为三个子问题。对于BS和ME-STARS波束成形子问题，采用了逐次凸近似方法。对于ME位置优化，将惩罚项纳入目标函数以处理约束，从而将原始问题转化为无约束形式。随后应用梯度下降算法迭代逼近次优解。数值结果表明：i）与具有固定位置元件的STARS辅助的NOMA通信相比，ME-STARS辅助的NOMA通信实现了优越的性能；ii）即使在受限的移动区域大小下，ME带来的性能增益也是显著的。
英文摘要
This paper proposes a movable-element (ME) enabled simultaneously transmitting and reflecting surface (ME-STARS)-assisted non-orthogonal multiple access (NOMA) communication system, where the positions of STARS elements can be flexibly reconfigured to introduce additional spatial degrees-of-freedom. Our objective is to maximize the sum achievable rate by jointly optimizing the active beamforming at the base station (BS) as well as the passive beamforming and MEs positions at the STARS. To tackle the resultant highly-coupled non-convex optimization problem, an alternating optimization-based algorithm is proposed, in which the original problem is decomposed into three subproblems. For the BS and ME-STARS beamforming subproblems, the successive convex approximation method is employed. For the MEs positions optimization, penalty terms are incorporated into the objective function to handle the constraints, thereby transforming the original problem into an unconstrained formulation. The gradient descent algorithm is subsequently applied to iteratively approach the sub-optimal solution. Numerical results demonstrate that: i) ME-STARS-assisted NOMA communications achieve superior performance compared to the NOMA communications assisted by STARS with fixed-position elements; and ii) the performance gain brought by the MEs is significant even with restricted moving region size.
