Radio Resource Selection in C-V2X Mode 4: A Multiagent Deep Reinforcement Learning Approach

The Third Generation Partnership Project has standardized cellular vehicle-to-everything (C-V2X) sidelink Mode 4 communication to support vehicle-to-vehicle safety applications. In Mode 4, the sensing-based semi-persistent scheduling (SPS) scheme allows vehicles to select radio resources autonomously. In particular, SPS has three steps to generate available resource lists (ARLs) for resource selection. However, the overlapping of ARLs is inevitable when radio resources are insufficient. In this case, randomly selecting from ARLs according to SPS is likely to make adjacent vehicles select the same resources, resulting in packet collisions. Unlike SPS, this paper proposes a multiagent deep reinforcement learning-based SPS (RL-SPS) algorithm to help vehicles select appropriate radio resources. As a consequence, packet collisions can be largely reduced. Furthermore, a centralized multi-head attention mechanism is adopted to improve the efficiency of the training process of RL-SPS. Simulation results demonstrate the reliability, scalability and robustness of the RL-SPS in a dynamic vehicular network.