VRLS: A Unified Reinforcement Learning Scheduler for Vehicle-to-Vehicle Communications
This addresses the challenge of maintaining V2V communication reliability for vehicles in areas without cellular coverage, though it is incremental as it builds on existing RL methods for scheduling.
The paper tackles the problem of reliable scheduling for vehicle-to-vehicle (V2V) communications in coverage gaps by proposing VRLS, a centralized reinforcement learning scheduler that predictively assigns resources while vehicles are in coverage, resulting in improved collision avoidance, half-duplex error reduction, and better resource reuse compared to state-of-the-art algorithms.
Vehicle-to-vehicle (V2V) communications have distinct challenges that need to be taken into account when scheduling the radio resources. Although centralized schedulers (e.g., located on base stations) could be utilized to deliver high scheduling performance, they cannot be employed in case of coverage gaps. To address the issue of reliable scheduling of V2V transmissions out of coverage, we propose Vehicular Reinforcement Learning Scheduler (VRLS), a centralized scheduler that predictively assigns the resources for V2V communication while the vehicle is still in cellular network coverage. VRLS is a unified reinforcement learning (RL) solution, wherein the learning agent, the state representation, and the reward provided to the agent are applicable to different vehicular environments of interest (in terms of vehicular density, resource configuration, and wireless channel conditions). Such a unified solution eliminates the necessity of redesigning the RL components for a different environment, and facilitates transfer learning from one to another similar environment. We evaluate the performance of VRLS and show its ability to avoid collisions and half-duplex errors, and to reuse the resources better than the state of the art scheduling algorithms. We also show that pre-trained VRLS agent can adapt to different V2V environments with limited retraining, thus enabling real-world deployment in different scenarios.