Joint Active RIS Configuration and User Power Control for Localization: A Neuroevolution-Based Approach
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914095969599488 |
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| author | Stamatelis, George Chen, Hui Wymeersch, Henk Alexandropoulos, George C. |
| author_facet | Stamatelis, George Chen, Hui Wymeersch, Henk Alexandropoulos, George C. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_13819 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Joint Active RIS Configuration and User Power Control for Localization: A Neuroevolution-Based Approach Stamatelis, George Chen, Hui Wymeersch, Henk Alexandropoulos, George C. Networking and Internet Architecture Machine Learning Multiagent Systems 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. |
| title | Joint Active RIS Configuration and User Power Control for Localization: A Neuroevolution-Based Approach |
| topic | Networking and Internet Architecture Machine Learning Multiagent Systems |
| url | https://arxiv.org/abs/2510.13819 |