{"ID":2852400,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.18978","arxiv_id":"2510.18978","title":"AI-Aided Annealed Langevin Dynamics for Rapid Optimization of Programmable Channels","abstract":"Emerging technologies such as Reconfigurable Intelligent Surfaces (RIS) 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 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.","short_abstract":"Emerging technologies such as Reconfigurable Intelligent Surfaces (RIS) 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 th...","url_abs":"https://arxiv.org/abs/2510.18978","url_pdf":"https://arxiv.org/pdf/2510.18978v1","authors":"[\"Tomer Shaked\",\"Philipp del Hougne\",\"George C. Alexandropoulos\",\"Nir Shlezinger\"]","published":"2025-10-21T18:01:56Z","proceeding":"eess.SP","tasks":"[\"eess.SP\"]","methods":"[]","has_code":false}