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

TL;DR

This paper introduces a deep reinforcement learning-based ensemble scheduler that adaptively selects among multiple algorithms to optimize resource allocation in mobile medical care communications, balancing performance and priority requirements.

Contribution

It presents a novel deep Q-Network approach for dynamic model selection in resource scheduling, integrating model-driven and data-driven methods for improved performance.

Findings

Effective in balancing mixed performance metrics.

Maximizes utility while prioritizing critical users.

Demonstrates adaptability in complex communication scenarios.

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.