Using Deep Reinforcement Learning for mmWave Real-Time Scheduling
This work addresses the problem of efficient real-time scheduling for 5G mmWave networks, which is incremental as it applies a known method (deep reinforcement learning) to a specific domain bottleneck.
The paper tackles real-time scheduling in multi-hop millimeter-wave meshes by developing a model-free deep reinforcement learning algorithm called AARL, which determines link activation and power levels within strict 5G time constraints, and demonstrates that AARL achieves higher throughput and faster decision-making compared to the benchmark RPMA algorithm across various topologies.
We study the problem of real-time scheduling in a multi-hop millimeter-wave (mmWave) mesh. We develop a model-free deep reinforcement learning algorithm called Adaptive Activator RL (AARL), which determines the subset of mmWave links that should be activated during each time slot and the power level for each link. The most important property of AARL is its ability to make scheduling decisions within the strict time slot constraints of typical 5G mmWave networks. AARL can handle a variety of network topologies, network loads, and interference models, it can also adapt to different workloads. We demonstrate the operation of AARL on several topologies: a small topology with 10 links, a moderately-sized mesh with 48 links, and a large topology with 96 links. We show that for each topology, we compare the throughput obtained by AARL to that of a benchmark algorithm called RPMA (Residual Profit Maximizer Algorithm). The most important advantage of AARL compared to RPMA is that it is much faster and can make the necessary scheduling decisions very rapidly during every time slot, while RPMA cannot. In addition, the quality of the scheduling decisions made by AARL outperforms those made by RPMA.