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Prediction of Resource Status in Medium Access Control for Vehicular Networks
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Publication Year
2024-01-01
Journal
IEEE Vehicular Technology Conference
Publisher
Institute of Electrical and Electronics Engineers Inc.
Citation
IEEE Vehicular Technology Conference
Keyword
GCNMedium Access ControlQoSResource ManagementVehicular Networks
Mesh Keyword
Autonomous drivingChannel resourceCommunications systemsConvolutional networksGraph convolutional networkMachine-learningMedium accessQuality-of-serviceResource managementVehicular networks
All Science Classification Codes (ASJC)
Computer Science ApplicationsElectrical and Electronic EngineeringApplied Mathematics
Abstract
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.
ISSN
1550-2252
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/37151
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85206207600&origin=inward
DOI
https://doi.org/10.1109/vtc2024-spring62846.2024.10683663
Type
Conference
Funding
This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government (MSIT) (No. 2023R1A2C1003783) and supported by Institute of Information & communications Technology Planning & Evaluation (IITP) under the Artificial Intelligence Convergence Innovation Human Resources Development (IITP-2024-RS-2023-00255968) grant funded by the Korea government (MSIT).
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Choi, Youngjune Image
Choi, Youngjune최영준
Department of Software and Computer Engineering
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