mlsanityData to 14 Oct 2026

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

arXiv 2510.13819 ↗First posted 25 Sep 2025cs.NI cs.LG cs.MAcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 12 of its 24 references that are in the map. First posted on arXiv 25 Sep 2025.

Builds on

12 of 24 references.

References · 12

  1. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,258
  2. 2017Evolution Strategies as a Scalable Alternative to Reinforcement Learningcited by 574
  3. 2022Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfacescited by 12
  4. 2021Active Sensing for Communications by Learningcited by 9
  5. 2023RISs and Sidelink Communications in Smart Cities: The Key to Seamless Localization and Sensingcited by 7
  6. 2023Active Sensing for Localization with Reconfigurable Intelligent Surfacecited by 6
  7. 2020Near-field Localization with a Reconfigurable Intelligent Surface Acting as Lenscited by 5
  8. 2022Leveraging RIS-Enabled Smart Signal Propagation for Solving Infeasible Localization Problemscited by 5
  9. 2022Deep Contextual Bandits for Orchestrating Multi-User MISO Systems with Multiple RISscited by 4
  10. 2023Active hypothesis testing in unknown environments using recurrent neural networks and model free reinforcement learningcited by 4
  11. 2023Localization with Reconfigurable Intelligent Surface: An Active Sensing Approachcited by 3
  12. 2022RIS-Enabled and Access-Point-Free Simultaneous Radio Localization and Mappingcited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025RIS-Enabled Smart Wireless Environments: Fundamentals and Distributed Optimizationcited by 0
  2. 2026Active Sensing for RIS-Aided Tracking and Power Control: A Hybrid Neuroevolution and Supervised Learning Approachcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Learning Beamforming Codebooks for Active Sensing with Reconfigurable Intelligent Surface
  2. 2026Parametrized Sharing for Multi-Agent Hybrid DRL for Multiple Multi-Functional RISs-Aided Downlink NOMA Networks
  3. 2024A Hierarchical DRL Approach for Resource Optimization in Multi-RIS Multi-Operator Networks
  4. 2024Multi-Agent Deep Reinforcement Learning for Energy Efficient Multi-Hop STAR-RIS-Assisted Transmissions
  5. 2026Active Sensing with Meta-Reinforcement Learning for Emitter Localization from RF Observations
  6. 2024Design Optimization of NOMA Aided Multi-STAR-RIS for Indoor Environments: A Convex Approximation Imitated Reinforcement Learning Approach
  7. 2026Aerial Multi-Functional RIS in Fluid Antennas-Aided Full-Duplex Networks: A Self-Optimized Hybrid Deep Reinforcement Learning Approach
  8. 2024An Overview of Machine Learning-Enabled Optimization for Reconfigurable Intelligent Surfaces-Aided 6G Networks: From Reinforcement Learning to Large Language Models