Graph-Based Distributed Control in Vehicular Communications Networks

This paper proposes a novel algorithm for allocating distributed spectrum and power resources in vehicular networks, including both vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication links. The proposed algorithm utilizes imitation learning to train distributed policies that adhere to the local structure of the vehicular system while imitating a centralized policy. This approach guarantees dependable, effective, and intelligent communication and control in the future generation of vehicular communication networks. The proposed model is evaluated through Matlab simulations and machine learning experiments, which demonstrate that the proposed scheme is more effective than traditional global optimization approaches, as it has a small transmission overhead and improves network performance by using graph-based distributed mode in vehicular networks.