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

• URL: http://arxiv.org/abs/2111.06680v1
• Date: Fri, 12 Nov 2021 12:04:53 GMT
• Title: Deep Reinforcement Model Selection for Communications Resource Allocation in On-Site Medical Care
• Authors: Steffen Gracla, Edgar Beck, Carsten Bockelmann, Armin Dekorsy
• Abstract summary: This paper explores a resource allocation scenario where a scheduler must balance mixed performance metrics among connected users. We present a scheduler that adaptively switches between different model-based scheduling algorithms. The resulting ensemble scheduler is able to combine its constituent algorithms to maximize a sum-utility cost function.
• Score: 8.564319625930892
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 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.

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