Scheduling Out-of-Coverage Vehicular Communications Using Reinforcement Learning

TL;DR

This paper introduces VRLS, a reinforcement learning-based centralized scheduler that improves out-of-coverage vehicle-to-vehicle communication reliability by proactively managing radio resources, outperforming existing distributed methods.

Contribution

The paper presents VRLS, a novel centralized reinforcement learning scheduler trained in simulation to enhance out-of-coverage V2V communication reliability without real-world retraining.

Findings

VRLS reduces packet error rate by 50% in high load conditions.

VRLS achieves near-maximum reliability in low load scenarios.

VRLS outperforms state-of-the-art distributed scheduling algorithms.

Abstract

Performance of vehicle-to-vehicle (V2V) communications depends highly on the employed scheduling approach. While centralized network schedulers offer high V2V communication reliability, their operation is conventionally restricted to areas with full cellular network coverage. In contrast, in out-of-cellular-coverage areas, comparatively inefficient distributed radio resource management is used. To exploit the benefits of the centralized approach for enhancing the reliability of V2V communications on roads lacking cellular coverage, we propose VRLS (Vehicular Reinforcement Learning Scheduler), a centralized scheduler that proactively assigns resources for out-of-coverage V2V communications \textit{before} vehicles leave the cellular network coverage. By training in simulated vehicular environments, VRLS can learn a scheduling policy that is robust and adaptable to environmental changes, thus eliminating the need for targeted (re-)training in complex real-life environments. We evaluate the performance of VRLS under varying mobility, network load, wireless channel, and resource configurations. VRLS outperforms the state-of-the-art distributed scheduling algorithm in zones without cellular network coverage by reducing the packet error rate by half in highly loaded conditions and achieving near-maximum reliability in low-load scenarios.