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

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.