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.

翻译：移动通信技术的更大能力使得以前没有规模的现场医疗护理能够相互连接。然而,将这种关键而艰巨的任务嵌入已经十分复杂的移动通信基础设施证明具有挑战性。本文件探讨了资源分配设想方案,其中调度员必须平衡相关用户的混合性性能衡量标准。为了完成这一资源分配任务,我们提出了一个在不同的基于模型的列表算法之间进行适应性转换的调度器。我们利用一个深层次的Q网络学习为特定情况选择一个时间安排模式的好处,将模式驱动和数据驱动方法的优势结合起来。由此产生的组合式调度仪能够将其构成算法结合起来,以最大限度地实现总效用成本功能,同时确保指定高优先用户的业绩。