面向智能反射面（RIS）辅助无线网络设计的随机优化框架
A Stochastic Optimization Framework for RIS-Aided Wireless Network Design
AI总结：
本文针对RIS配置优化的大规模非凸问题，提出基于CE和MH连续版本的随机优化框架，其性能优于或相当的同时执行时间缩短10倍，可应用于近无源与有源RIS的速率及能效最大化。
AI中文摘要：
智能反射面（RIS）是一种利用超表面提升未来无线网络频谱和能量效率的极具前景的技术，然而优化RIS配置通常会产生大规模非凸问题，其复杂度随散射元件数量及先进超表面架构的采用显著增长。本文提出一种基于交叉熵（CE）和梅特罗波利斯-黑斯廷斯（MH）方法连续版本的RIS辅助无线网络随机优化框架。与现有主要聚焦离散优化的随机方法不同，该框架直接处理连续变量，可通过松弛和投影轻松应用于离散场景。本文对所提算法进行理论刻画，包括收敛性保证与效率分析。该框架应用于两类任务：（i）近无源RIS的可达速率最大化，（ii）有源RIS的能量效率最大化。数值结果表明，所提方法性能与最先进的确定性算法相当或更优，且在代表性场景中执行时间最多可缩短10倍。
英文摘要：
Reconfigurable intelligent surfaces (RISs) are a promising technology for improving the spectral and energy efficiency of future wireless networks, which make use of metasurfaces. However, optimizing RIS configurations typically leads to large-scale, non-convex problems whose complexity grows significantly with the number of scattering elements and the adoption of advanced metasurface architectures. In this paper, we develop a stochastic optimization framework for RIS-aided wireless networks based on continuous versions of the ($a$) cross-entropy (CE) and ($b$) Metropolis-Hastings (MH) methods. Unlike existing stochastic approaches that mainly focus on discrete optimization, the proposed framework directly handles continuous variables and can be readily applied to discrete settings through relaxation and projection. We provide a theoretical characterization of the proposed algorithms, including convergence guarantees and efficiency analysis. The framework is applied to ($i$) achievable-rate maximization with nearly-passive RISs and ($ii$) energy-efficiency maximization with active RISs. Numerical results show that the proposed methods achieve performance comparable to, or better than, state-of-the-art deterministic algorithms, while reducing execution times up to 10 times in representative scenarios.
