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New Dual-Agent Deep Learning Framework Optimizes RIS-Aided Mobile User Tracking

By PulseAugur Editorial · [3 sources] ·

Researchers have developed a novel Dual-Agent (DA) deep learning framework to optimize energy efficiency in tracking power-limited mobile users with the aid of Reconfigurable Intelligent Surfaces (RIS). This approach integrates neuroevolution and supervised learning to jointly manage RIS phase profiles and user transmit power in real-time, overcoming challenges with discrete phase responses and single-bit feedback. Numerical simulations indicate the DA framework significantly outperforms existing methods like Kalman filters, particle filters, and traditional fingerprinting schemes in both tracking and static localization scenarios. AI

IMPACT This framework could lead to more energy-efficient wireless communication systems by optimizing resource allocation.

RANK_REASON This is a research paper detailing a novel deep learning framework for a specific technical problem. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.MA (Multiagent) →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

New Dual-Agent Deep Learning Framework Optimizes RIS-Aided Mobile User Tracking

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This is a research paper detailing a novel deep learning framework for a specific technical problem. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [3]

  1. arXiv cs.AI TIER_1 English(EN) · George Stamatelis, Hui Chen, Henk Henk Wymeersch, George C. Alexandropoulos ·

    Active Sensing for RIS-Aided Tracking and Power Control: A Hybrid Neuroevolution and Supervised Learning Approach

    arXiv:2607.00056v1 Announce Type: cross Abstract: This paper studies energy efficient tracking of power-limited mobile users with the assistance of a Reconfigurable Intelligent Surface (RIS). Since localization pilot transmissions dominate the energy budget of power-constrained d…

  2. arXiv cs.MA (Multiagent) TIER_1 English(EN) · George C. Alexandropoulos ·

    Active Sensing for RIS-Aided Tracking and Power Control: A Hybrid Neuroevolution and Supervised Learning Approach

    This paper studies energy efficient tracking of power-limited mobile users with the assistance of a Reconfigurable Intelligent Surface (RIS). Since localization pilot transmissions dominate the energy budget of power-constrained devices, we introduce a low-overhead feedback link …

  3. arXiv cs.MA (Multiagent) TIER_1 English(EN) · George C. Alexandropoulos ·

    Active Sensing for RIS-Aided Tracking and Power Control: A Hybrid Neuroevolution and Supervised Learning Approach

    This paper studies energy efficient tracking of power-limited mobile users with the assistance of a Reconfigurable Intelligent Surface (RIS). Since localization pilot transmissions dominate the energy budget of power-constrained devices, we introduce a low-overhead feedback link …

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