Deep Reinforcement Model Selection for Communications Resource Allocation in On-Site Medical Care

Autoren: S. Gracla, E. Beck, C. Bockelmann, A. Dekorsy
Kurzfassung:

Greater capabilities of mobile communications technology enable interconnection of on-site medical care at a scale previously unavailable. However, embedding such critical, demanding tasks into the already complex infrastructure of mobile communications proves challenging. This paper explores a resource allocation scenario where a scheduler must balance mixed performance metrics among connected users. To fulfill this resource allocation task, we present a scheduler that adaptively switches between different model-based scheduling algorithms. We make use of a deep Q-Network (DQN) to learn the benefit of selecting a scheduling paradigm for a given situation, combining advantages from model-driven and data-driven approaches. The resulting ensemble scheduler is able to combine its constituent algorithms to maximize a sum-utility cost function while ensuring performance on designated high-priority users.

Dokumenttyp: Konferenzbeitrag
Veröffentlichung: 13. April 2022
Konferenz: IEEE WCNC 2022
Dateien:
Deep Reinforcement Model Selection for Communications Resource Allocation in On-Site Medical Care
00_paper_dqn_model_selection_ant_website.pdf284 KB
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