6 research outputs found
Reinforcement Learning Scheduler for Vehicle-to-Vehicle Communications Outside Coverage
Radio resources in vehicle-to-vehicle (V2V) communication can be scheduled
either by a centralized scheduler residing in the network (e.g., a base station
in case of cellular systems) or a distributed scheduler, where the resources
are autonomously selected by the vehicles. The former approach yields a
considerably higher resource utilization in case the network coverage is
uninterrupted. However, in case of intermittent or out-of-coverage, due to not
having input from centralized scheduler, vehicles need to revert to distributed
scheduling. Motivated by recent advances in reinforcement learning (RL), we
investigate whether a centralized learning scheduler can be taught to
efficiently pre-assign the resources to vehicles for out-of-coverage V2V
communication. Specifically, we use the actor-critic RL algorithm to train the
centralized scheduler to provide non-interfering resources to vehicles before
they enter the out-of-coverage area. Our initial results show that a RL-based
scheduler can achieve performance as good as or better than the state-of-art
distributed scheduler, often outperforming it. Furthermore, the learning
process completes within a reasonable time (ranging from a few hundred to a few
thousand epochs), thus making the RL-based scheduler a promising solution for
V2V communications with intermittent network coverage.Comment: Article published in IEEE VNC 201