Learning Resource Scheduling with High Priority Users using Deep Deterministic Policy Gradients
This addresses the need for reliable and efficient scheduling in mobile communications for critical applications like healthcare, but it is incremental as it adapts an existing DDPG method to a specific domain.
The paper tackles the problem of communications resource scheduling for mobile networks with high-priority users, such as in healthcare applications, by applying Deep Deterministic Policy Gradient (DDPG) methods, achieving high performance on a flexible sum-utility goal.
Advances in mobile communication capabilities open the door for closer integration of pre-hospital and in-hospital care processes. For example, medical specialists can be enabled to guide on-site paramedics and can, in turn, be supplied with live vitals or visuals. Consolidating such performance-critical applications with the highly complex workings of mobile communications requires solutions both reliable and efficient, yet easy to integrate with existing systems. This paper explores the application of Deep Deterministic Policy Gradient~(\ddpg) methods for learning a communications resource scheduling algorithm with special regards to priority users. Unlike the popular Deep-Q-Network methods, the \ddpg is able to produce continuous-valued output. With light post-processing, the resulting scheduler is able to achieve high performance on a flexible sum-utility goal.