AI-Aided Annealed Langevin Dynamics for Rapid Optimization of Programmable Channels

Emerging technologies such as reconfigurable intelligent surfaces (RISs) make it possible to optimize some parameters of wireless channels. Conventional approaches require relating the channel and its programmable parameters via a simple model that supports rapid optimization, e.g., re-tuning the parameters each time the users move. However, in practice such models are often crude approximations of the channel, and a more faithful description can be obtained via complex simulators, or only by measurements. In this work, we introduce a novel approach for rapid optimization of programmable channels based on AI -aided annealed Langevin dynamics (ALD), which bypasses the need for explicit channel modeling. By framing the ALD algorithm using the maximum a-posteriori probability (MAP) estimate, we design a deep unfolded ALD algorithm that leverages a deep neural network (DNN) to estimate score gradients for optimizing channel parameters. We introduce a training method that overcomes the need for channel modeling using zero-order gradients, combined with active learning to enhance generalization, enabling optimization in complex and dynamically changing environments. We evaluate the proposed method in RIS-aided scenarios subject to rich-scattering effects. Our results demonstrate that our AI -aided ALD method enables rapid and reliable channel parameter tuning with limited latency.