A novel dual-agent framework with hybrid neuroevolution and supervised learning optimizes RIS phases and UE power for accurate real-time tracking in RIS-aided systems, outperforming traditional filters and ML baselines in simulations.
Active hypothesis testing in un- known environments using recurrent neural networks and model free reinforcement learning,
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Active Sensing for RIS-Aided Tracking and Power Control: A Hybrid Neuroevolution and Supervised Learning Approach
A novel dual-agent framework with hybrid neuroevolution and supervised learning optimizes RIS phases and UE power for accurate real-time tracking in RIS-aided systems, outperforming traditional filters and ML baselines in simulations.