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) →
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