This paper proposes a novel multi-agent reinforcement learning (MARL) framework to optimize energy efficiency in RIS-aided cell-free massive MIMO systems by jointly addressing access point selection, precoding, and RIS beamforming.
Optimizing energy efficiency in SWIPT systems using active STAR-RIS technology.
Edge computing resource allocation methods are improved through a distributed approach with preemption, enhancing system-wide performance by 20-25%.
Optimizing resource allocation in mobile edge computing through deep reinforcement learning for efficient task graph offloading.
Proposing algorithms for energy-efficient communication in high-frequency systems through cooperative rate-splitting.