{"ID":2865822,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.13819","arxiv_id":"2510.13819","title":"Joint Active RIS Configuration and User Power Control for Localization: A Neuroevolution-Based Approach","abstract":"This paper studies user localization aided by a Reconfigurable Intelligent Surface (RIS). A feedback link from the Base Station (BS) to the user is adopted to enable dynamic power control of the user pilot transmissions in the uplink. A novel multi-agent algorithm for the joint control of the RIS phase configuration and the user transmit power is presented, which is based on a hybrid approach integrating NeuroEvolution (NE) and supervised learning. The proposed scheme requires only single-bit feedback messages for the uplink power control, supports RIS elements with discrete responses, and is numerically shown to outperform fingerprinting, deep reinforcement learning baselines and backpropagation-based position estimators.","short_abstract":"This paper studies user localization aided by a Reconfigurable Intelligent Surface (RIS). A feedback link from the Base Station (BS) to the user is adopted to enable dynamic power control of the user pilot transmissions in the uplink. A novel multi-agent algorithm for the joint control of the RIS phase configuration an...","url_abs":"https://arxiv.org/abs/2510.13819","url_pdf":"https://arxiv.org/pdf/2510.13819v1","authors":"[\"George Stamatelis\",\"Hui Chen\",\"Henk Wymeersch\",\"George C. Alexandropoulos\"]","published":"2025-09-25T07:36:04Z","proceeding":"cs.NI","tasks":"[\"cs.NI\",\"cs.LG\",\"cs.MA\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}