Optimization for Massive 3D-RIS Deployment: A Generative Diffusion Model-Based Approach

2025-09-15

AI Summary

To address high computational complexity, poor environmental adaptability, and susceptibility to local optima in large-scale 3D reconfigurable intelligent surface (RIS) deployment optimization, this work formulates the deployment problem as a conditional generative task for the first time and proposes a probabilistic optimization framework based on diffusion models. By discretizing the 3D space into a grid and jointly modeling multiple RIS units, the method leverages the iterative denoising process of diffusion models to generate globally optimal, robust deployment strategies efficiently. This paradigm shift transcends conventional deterministic optimization approaches, significantly improving adaptability to dynamic channel conditions and generalization across unseen scenarios. Simulation results demonstrate that the proposed method outperforms state-of-the-art baselines in coverage gain, deployment efficiency, and cross-scenario generalization, establishing a novel, data-driven paradigm for intelligent, large-scale RIS deployment.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Reconfigurable Intelligent Surfaces (RISs) transform the wireless environment by modifying the amplitude, phase, and polarization of incoming waves, significantly improving coverage performance. Notably, optimizing the deployment of RISs becomes vital, but existing optimization methods face challenges such as high computational complexity, limited adaptability to changing environments, and a tendency to converge on local optima. In this paper, we propose to optimize the deployment of large-scale 3D RISs using a diffusion model based on probabilistic generative learning. We begin by dividing the target area into fixed grids, with each grid corresponding to a potential deployment location. Then, a multi-RIS deployment optimization problem is formulated, which is difficult to solve directly. By treating RIS deployment as a conditional generation task, the well-trained diffusion model can generate the distribution of deployment strategies, and thus, the optimal deployment strategy can be obtained by sampling from this distribution. Simulation results demonstrate that the proposed diffusion-based method outperforms traditional benchmark approaches in terms of exceed ratio and generalization.

Problem

• Optimizing large-scale 3D RIS deployment locations
• Overcoming computational complexity and local optima limitations
• Generating optimal deployment strategies using diffusion models

Innovation

• Diffusion model for 3D-RIS deployment optimization
• Probabilistic generative learning for strategy distribution
• Conditional generation approach sampling optimal deployment