Scheduling Out-of-Coverage Vehicular Communications Using Reinforcement Learning
This addresses the problem of inefficient communication scheduling for vehicles in out-of-coverage areas, offering a significant improvement over existing distributed methods.
The paper tackles the problem of unreliable vehicle-to-vehicle communications in areas without cellular coverage by proposing VRLS, a centralized scheduler that proactively assigns resources before vehicles leave coverage, resulting in a halved packet error rate in high-load conditions and near-maximum reliability in low-load scenarios.
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.