Joint Active RIS Configuration and User Power Control for Localization: A Neuroevolution-Based Approach
This work addresses localization accuracy in wireless networks, which is an incremental improvement over existing methods.
The paper tackles the problem of user localization using Reconfigurable Intelligent Surfaces (RIS) by developing a multi-agent algorithm that jointly controls RIS phase configuration and user transmit power, which outperforms fingerprinting, deep reinforcement learning baselines, and backpropagation-based position estimators in numerical simulations.
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.