A Stochastic Optimization Framework for RIS-Aided Wireless Network Design

2026-07-30

AI Summary

This work addresses the challenge of rapidly escalating computational complexity in large-scale non-convex reconfigurable intelligent surface (RIS) configuration optimization, which intensifies with the number of scattering elements and architectural intricacy. To tackle this, the paper proposes a stochastic optimization framework that integrates continuous cross-entropy (CE) methods with Metropolis–Hastings (MH) sampling. The approach operates directly on continuous variables and incorporates relaxation and projection mechanisms to accommodate discrete RIS configurations, making it applicable to both nearly passive and active RIS architectures for optimizing spectral efficiency and energy efficiency. As the first systematic application of continuous stochastic optimization to RIS network design, the proposed method transcends the limitations of conventional discrete optimization, offering theoretical guarantees on convergence and computational efficiency. In representative scenarios, it achieves performance comparable to or better than state-of-the-art deterministic algorithms while reducing runtime by up to an order of magnitude.

Abstract

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.

Problem

• Reconfigurable Intelligent Surfaces
• Stochastic Optimization
• Non-convex Optimization
• Wireless Network Design
• Metasurfaces

Innovation

• stochastic optimization
• reconfigurable intelligent surfaces
• cross-entropy method
• Metropolis-Hastings algorithm
• continuous optimization