Prediction of Resource Status in Medium Access Control for Vehicular Networks - Deng, Yafeng; Choi, Young June (IEEE Vehicular Technology Conference, 2024)
Vehicular networks draw much attention as the essential communication system of vehicles, especially with the development of autonomous driving. The channel resource is shared and also contented by each user, therefore, quality of service (QoS) is hard to be guaranteed. Existing solutions, especially machine learning based algorithms [1], cannot fully address dynamic neighbor's status because the feature size varies according to the varying number of neighbors. In this work, we make a practical dataset for resource allocation purpose using ns-3 and SUMO, which has been mainly used for vehicle-to-vehicle communication. Various features are collected. Furthermore, a graph convolutional network (GCN) [2] is used to perform the classification of transmission status, success or failure. The prediction accuracy is improved by 15% than that of LSTM thanks to the graph representation of data, which implies an alleviation of packets collision.
Keywords: Autonomous driving; Channel resource; Communications systems; Convolutional networks; Graph convolutional network; Machine-learning; Medium access; Quality-of-service; Resource management; Vehicular networks; GCN; Medium Access Control; QoS; Resource Management; Vehicular Networks