利用FARIS解锁下行NOMA：联合聚类与表面配置设计
Unlocking Downlink NOMA with FARIS: Joint Clustering and Surface Configuration Design
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中文总结 AI 辅助
该研究针对FARIS辅助的下行NOMA系统，提出两阶段优化框架，联合优化用户聚类等多参数，验证了其在高速下行传输中的有效性，性能优于基准方案。
中文摘要 AI 辅助
本文研究流体型可重构智能表面（FARIS）辅助的下行非正交多址（NOMA）系统。我们构建网络总速率最大化问题，在服务质量、反射功率及硬件约束下，联合优化用户聚类、NOMA功率分配、FARIS放大增益、离散相移及流体单元选择。为求解该非凸混合整数问题，我们提出两阶段框架：基于距离的交错聚类构建 successive-interference-cancellation（SIC，串行干扰消除）友好型用户组，以及按簇交替优化。各子问题采用几何规划（GP）、分式规划（FP）、带混合整数相移优化的 majorization-minimization（MM）及交叉熵方法（CEM）处理。数值结果显示收敛迅速，相较基于穷举搜索（BFS）的最优方案性能接近最优，且始终优于基准方案。这些结果验证了FARIS与NOMA集成用于高速下行传输的有效性。
英文摘要
This paper investigates a fluid active reconfigurable intelligent surface (FARIS)-aided downlink non-orthogonal multiple access (NOMA) system. We formulate a network sum-rate maximization problem that jointly optimizes user clustering, NOMA power allocation, FARIS amplification gains, discrete phase shifts, and fluid element selection under quality-of-service, reflected-power, and hardware constraints. To address the resulting nonconvex mixed-integer problem, we develop a two-stage framework comprising distance-based interleaved clustering for constructing successive-interference-cancellation (SIC)-friendly user groups and per-cluster alternating optimization. The resulting subproblems are handled using geometric programming (GP), fractional programming (FP), majorization-minimization (MM) with mixed-integer phase optimization, and the cross-entropy method (CEM). Numerical results demonstrate rapid convergence, near-optimal performance relative to brute-force search (BFS)-based optimum, and consistently outperforms the benchmarks. These results verify the effectiveness of jointly integrating FARIS and NOMA for high-rate downlink transmission.
