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.
@article{arxiv.2510.13819,
title = {Joint Active RIS Configuration and User Power Control for Localization: A Neuroevolution-Based Approach},
author = {George Stamatelis and Hui Chen and Henk Wymeersch and George C. Alexandropoulos},
journal= {arXiv preprint arXiv:2510.13819},
year = {2025}
}