Joint Active RIS Configuration and User Power Control for Localization: A Neuroevolution-Based Approach

2025/09/25 by Stamatelis, George, Chen, Hui, Wymeersch, Henk +1 · 1 citation
#FOS: Computer and information sciences #Machine Learning (cs.LG) #Multiagent Systems (cs.MA) #Networking and Internet Architecture (cs.NI)

paper · doi:10.48550/arxiv.2510.13819

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.

Citations

• Localization with Reconfigurable Intelligent Surface: An Active Sensing Approach
• Active Sensing for Localization with Reconfigurable Intelligent Surface
• Active hypothesis testing in unknown environments using recurrent neural networks and model free reinforcement learning
• RIS-Enabled Smart Wireless Environments: Deployment Scenarios, Network Architecture, Bandwidth and Area of Influence
• RISs and Sidelink Communications in Smart Cities: The Key to Seamless Localization and Sensing
• RIS-Enabled and Access-Point-Free Simultaneous Radio Localization and Mapping
• Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfaces
• Near-Field Hierarchical Beam Management for RIS-Enabled Millimeter Wave Multi-Antenna Systems
• Deep Contextual Bandits for Orchestrating Multi-User MISO Systems with Multiple RISs
• Active Sensing for Communications by Learning
• Deep Active Learning Approach to Adaptive Beamforming for mmWave Initial Alignment
• Near-field Localization with a Reconfigurable Intelligent Surface Acting as Lens
• Evolution Strategies as a Scalable Alternative to Reinforcement Learning
• Asynchronous Methods for Deep Reinforcement Learning
• Long Short-Term Memory
• The Complexity of Markov Decision Processes

Cited by

• RIS-Enabled Smart Wireless Environments: Fundamentals and Distributed Optimization

Related

• Localization with Reconfigurable Intelligent Surface: An Active Sensing Approach
• Active Sensing for Localization with Reconfigurable Intelligent Surface
• Multi-Branch Attention Convolutional Neural Network for Online RIS Configuration with Discrete Responses: A Neuroevolution Approach
• Employing High-Dimensional RIS Information for RIS-aided Localization Systems
• Phase Configuration Learning in Wireless Networks with Multiple\n Reconfigurable Intelligent Surfaces