Multi-agent deep reinforcement learning based resource management in heterogeneous V2X networks

Abstract

In Heterogeneous Vehicle-to-Everything Networks (HVNs), multiple users such as vehicles and handheld devices and infrastructure can communicate with each other to obtain more advanced services. However, the increasing number of entities accessing HVNs presents a huge technical challenge to allocate the limited wireless resources. Traditional model-driven resource allocation approaches are no longer applicable because of rich data and the interference problem of multiple communication modes reusing resources in HVNs. In this paper, we investigate a wireless resource allocation scheme including power control and spectrum allocation based on the resource block reuse strategy. To meet the high capacity of cellular users and the high reliability of Vehicle-to-Vehicle (V2V) user pairs, we propose a data-driven Multi-Agent Deep Reinforcement Learning (MADRL) resource allocation scheme for the HVN. Simulation results demonstrate that compared to existing algorithms, the proposed MADRL-based scheme achieves a high sum capacity and probability of successful V2V transmission, while providing close-to-limit performance.