@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}, publisher = {IEEE}, URL = {https://wcnc2022.ieee-wcnc.org/}, address={Austin/Online, USA}, 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 Wireless Communications and Networking Conference} }