Using Deep Reinforcement Learning for mmWave Real-Time Scheduling

TL;DR

This paper introduces a deep reinforcement learning algorithm, AARL, for real-time scheduling in multi-hop mmWave networks, demonstrating superior speed and decision quality over traditional algorithms across various network topologies.

Contribution

The paper presents a novel model-free deep reinforcement learning algorithm, AARL, capable of rapid, adaptive scheduling decisions in mmWave networks, outperforming existing methods in speed and effectiveness.

Findings

AARL achieves higher throughput than benchmark algorithms.

AARL makes faster scheduling decisions within strict time constraints.

AARL adapts effectively to different network topologies and loads.

Abstract

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.