Author

Sungweon Hong

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Oct 2026

LyMARL: A Lyapunov-Guided Multiagent Reinforcement Learning Framework for Energy-Aware Radio Resource Management

Energy-efficient control in dense wireless networks requires jointly optimizing user association, scheduling, and base station (BS) activation under finite energy budgets. Existing Lyapunov-based approaches enforce energy constraints only asymptotically. Under finite-horizon operation, the myopic per-slot drift-plus-pe...

Wonhyeok Ko, Yeonseo Jeong, Sungweon Hong et al. · IEEE Internet of Things Jour... · 1 citation

Preprint Aug 2026

Heterogeneous Multi-Agent Reinforcement Learning for Radio Resource Management under Coupled Finite-Horizon Constraints

Simulations show that HeLyMARL is the only method that sustains the throughput-fairness balance together with uninterrupted service throughout the horizon, outperforming conventional MARL, Lyapunov-based, and constrained MARL benchmarks without premature budget exhaustion.

Yeonseo Jeong, Wonhyeok Ko, Sungweon Hong et al. · 0 citations