A Reinforcement Learning Approach for Scheduling in mmWave Networks

TL;DR

This paper presents a reinforcement learning-based scheduling method for mmWave networks that maintains communication rates despite link failures and blockages, without requiring prior network knowledge.

Contribution

It introduces a SAC-based reinforcement learning approach for resilient scheduling in mmWave networks, adapting to failures without network topology or capacity information.

Findings

Achieves desired communication rate in dynamic, failure-prone environments

Demonstrates robustness against link blockage

Operates without prior network knowledge

Abstract

We consider a source that wishes to communicate with a destination at a desired rate, over a mmWave network where links are subject to blockage and nodes to failure (e.g., in a hostile military environment). To achieve resilience to link and node failures, we here explore a state-of-the-art Soft Actor-Critic (SAC) deep reinforcement learning algorithm, that adapts the information flow through the network, without using knowledge of the link capacities or network topology. Numerical evaluations show that our algorithm can achieve the desired rate even in dynamic environments and it is robust against blockage.