@inproceedings{
  author = {S. Gracla and E. Beck and C. Bockelmann and A. Dekorsy},
  year = {2022},
  month = {Apr},
  title = {Deep Reinforcement Model Selection for Communications Resource Allocation in On-Site Medical Care},
  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 (DQN) 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.},
  booktitle={IEEE WCNC 2022}
}