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arXiv Machine Learning By Yeonseo Jeong, Wonhyeok Ko, Sungweon Hong, Songnam Hong

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

Read the original on arXiv Machine Learning →

arXiv:2608. 01745v1 Announce Type: new Abstract: Maximizing throughput under proportional fairness in dense wireless networks requires jointly managing user association, scheduling, base station (BS) activation, and handover control under hard finite-horizon energy and handover budgets, which induces a fundamental tension between BS-side energy management and user-side handover regulation.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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