Distributed and Coordinated Multi-Agent Learning for Resource Allocation in Vehicular Networks

In cellular-based vehicular communication networks, a V2V link shares the same spectrum resources with a V2I link, resulting in mutual interference and adversely affecting vehicular communication performance. The existing resource allocation strategies in cellular-based vehicular communication networks rely on individual links to adjust network parameters, leading to unfair resource allocation. To resolve this problem, we propose a joint spectrum sharing and power control strategy based on deep reinforcement learning is proposed. Firstly, a coordination graph is designed to describe the dependencies among V2V links. Then, a graph attention mechanism is proposed to extract the relevant information from neighboring V2V links. Finally, based on the multi-agent deep deterministic policy gradient method, a coordination learning algorithm for wireless resource allocation in cellular-based vehicular networks is established. Experimental results show that the proposed method can improve the transmission rate of V2I links and the successful transmission rate of V2V links.