Abstract
Vehicle-to-everything (V2X) communication is an essential technology for future vehicular applications. It is challenging to simultaneously achieve vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communications, given the shared spectrum. Deep reinforcement learning (DRL)-based algorithms have been proposed for resource allocation in V2I and V2V designs. Existing DRL designs focus on the objectives of high-capacity V2I and high-reliability V2V links. In this study, a multi-agent DRL algorithm is proposed to maximize the sum capacity of V2I links while ensuring capacity fairness among the V2V links. The simulation results demonstrate the balance between the V2I-V2V objectives achieved by the proposed algorithm.