AI 中文总结
针对RIS辅助RSMA-ISAC系统的联合优化问题，提出基于约束分离的交替优化算法，在保持感知性能与基准相当的同时大幅降低计算复杂度，且优于传统SDMA方案。
AI 中文摘要
本文研究以感知为中心的可重构智能表面（RIS）辅助的速率分割多址接入-一体化感知与通信（RSMA-ISAC）系统设计。具体而言，我们提出一种新的波束增益近似方法，以在满足通信服务质量（QoS）的同时提升感知波束增益。由于波束成形向量与RIS相移的联合优化高度耦合且非凸，现有方法通常依赖通用优化求解器，计算复杂度极高。为解决该问题，我们提出一种高效的基于约束分离的交替优化算法（CS-AO）。该算法可有效解耦优化变量，为所有子问题提供闭式解，从而显著降低计算负担。仿真结果表明，所提算法的感知波束增益性能与逐次凸近似（SCA）、半定松弛（SDR）基准相当，同时运行时间分别缩短120倍以上和50倍以上；此外，与传统空分多址接入（SDMA）方案相比，所提设计展现出显著的感知波束增益。
英文摘要
This paper investigates the sensing-centric design of reconfigurable intelligent surface (RIS)-enabled rate-splitting multiple access-integrated sensing and communication (RSMA-ISAC) systems. Specifically, we propose a new beam-gain approximation method to enhance the sensing beam gain while satisfying communication quality-of-service (QoS) constraints.Since the joint optimization of the beamforming vectors and RIS phase shifts is highly coupled and non-convex, existing methods typically rely on generic optimization solvers involving substantial computational complexity. To address this issue, we propose an efficient constraints-separation-based alternating optimization algorithm (CS-AO). Our proposed algorithm effectively decouples the optimization variables and yields closed-form solutions for all subproblems, thereby significantly reducing the computational burden. Simulation results show that the proposed algorithm achieves sensing beam-gain performance comparable to successive convex approximation (SCA) and semidefinite relaxation (SDR) benchmarks, while achieving more than 120-fold and 50-fold runtime reductions. In addition, compared with conventional space-division multiple access (SDMA) schemes, the proposed design exhibits substantial sensing beam gain.
Journal refIEEE Wireless Communications Letters, 2026
