Resource Allocation based on Graph Neural Networks in Vehicular Communications

Abstract

In this article, we investigate spectrum allocation in vehicle-to-everything (V2X) network. We first express the V2X network into a graph, where each vehicle-to-vehicle (V2V) link is a node in the graph. We apply a graph neural network (GNN) to learn the low-dimensional feature of each node based on the graph information. According to the learned feature, multi-agent reinforcement learning (RL) is used to make spectrum allocation. Deep Q-network is utilized to learn to optimize the sum capacity of the V2X network. Simulation results show that the proposed allocation scheme can achieve near-optimal performance.