Resource Allocation based on Graph Neural Networks in Vehicular Communications
Ziyan He, Liang Wang, Hao Ye, Geoffrey Ye Li, Biing‐Hwang Juang
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.
Topics & Concepts
Computer scienceGraphReinforcement learningResource allocationNode (physics)Artificial neural networkComputer networkArtificial intelligenceDistributed computingTheoretical computer scienceEngineeringStructural engineeringVehicular Ad Hoc Networks (VANETs)Advanced MIMO Systems OptimizationAge of Information Optimization