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.

翻译：我们研究了多跳毫米波网状网络中的实时调度问题。我们开发了一种无模型的深度强化学习算法，称为自适应激活器强化学习（AARL），该算法可确定每个时隙内应激活的毫米波链路子集以及每条链路的功率水平。AARL最重要的特性是，它能够在典型5G毫米波网络的严格时隙约束内做出调度决策。AARL可处理多种网络拓扑、网络负载和干扰模型，并能适应不同的工作负载。我们在多种拓扑上演示了AARL的运行情况：包含10条链路的小型拓扑、包含48条链路的中等规模网状网络，以及包含96条链路的大型拓扑。我们针对每种拓扑，将AARL获得的吞吐量与基准算法RPMA（残差利润最大化算法）进行了比较。与RPMA相比，AARL最重要的优势在于其速度更快，能够在每个时隙内非常迅速地做出必要的调度决策，而RPMA则无法做到。此外，AARL做出的调度决策质量也优于RPMA。