Deep Reinforcement Model Selection for Communications Resource Allocation in On-Site Medical Care
This addresses resource allocation challenges in mobile communications for critical on-site medical care, though it is incremental as it combines existing model-driven and data-driven approaches.
The paper tackled the problem of resource allocation for on-site medical care communications by developing a scheduler that adaptively switches between model-based algorithms using a deep Q-Network to maximize a sum-utility cost function while ensuring performance for high-priority users.
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.