Deep Reinforcement Learning for Scheduling and Power Allocation in a 5G Urban Mesh. (arXiv:2210.01423v1 [cs.NI])

We study the problem of routing and scheduling of real-time flows over a
multi-hop millimeter wave (mmWave) mesh. We develop a model-free deep
reinforcement learning algorithm that determines which subset of the mmWave
links should be activated during each time slot and using what power level. The
proposed algorithm, called Adaptive Activator RL (AARL), can handle a variety
of network topologies, network loads, and interference models, as well as adapt
to different workloads. We demonstrate the operation of AARL on …

arxiv mesh power reinforcement reinforcement learning scheduling