HiRL applies hierarchical RL to coordinate power and task decisions in heterogeneous edge environments, delivering 28% lower latency than Single-DDQN and up to 51% energy savings under low load while maintaining near-100% task completion.
Noncooperative cellular wireless with unlimited num- bers of base station antennas
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Proposes a three-layer framework using formal AI reasoning for verification, derivation, and discovery in wireless communications theory.
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HiRL: Hierarchical Reinforcement Learning for Coordinated Resource Management in Heterogeneous Edge Computing
HiRL applies hierarchical RL to coordinate power and task decisions in heterogeneous edge environments, delivering 28% lower latency than Single-DDQN and up to 51% energy savings under low load while maintaining near-100% task completion.
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Rethinking Wireless Communications through Formal Mathematical AI Reasoning
Proposes a three-layer framework using formal AI reasoning for verification, derivation, and discovery in wireless communications theory.