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Multiple QoS Enabled Intelligent Resource Management in Vehicle-to-Vehicle Communication
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dc.contributor.authorDeng, Yafeng-
dc.contributor.authorPaul, Rajib-
dc.contributor.authorChoi, Young June (researcherId=7406117220; isni=0000000405323933; orcid=https://orcid.org/0000-0003-2014-6587)-
dc.date.issued2024-01-01-
dc.identifier.issn1558-0016-
dc.identifier.urihttps://aurora.ajou.ac.kr/handle/2018.oak/34016-
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85187020077&origin=inward-
dc.description.abstractVehicular networks have stringent quality of service (QoS) requirements in terms of reliability, throughput, and latency. With the emergence of diverse services for autonomous driving, the resource contention in vehicle-to-vehicle communication can cause unavoidable packet loss and, therefore, must be handled for safety. Moreover, different levels of QoS should be defined for each service and task; however, existing solutions can neither provide a resource allocation scheme for any level of QoS requirement nor serve new stringent services without configuration or modification. We propose a distributed hierarchical deep Q-network (DH-DQN) to handle resource contention specifically. Thus, an intelligence resource management (I-RM) scheme is designed to serve on-demand QoSs. We first formulate the problem to address multiple QoS requirements, which extends the coverage of resource management tasks for on-demand stringent services. From the perspective of transmission pattern, we designed a hierarchical DQN structure that deals with resource block contention in a fully distributed manner and a state-action framework that enables a numerically defined service demand. In addition, a target epsilon -greedy is proposed to accelerate convergence, and a modified transfer learning algorithm is used to enhance learning performance for various levels of service. Through extensive simulations, we demonstrated that the proposed DH-DQN can learn successful transmission patterns to meet different levels of multiple QoS requirements.-
dc.description.sponsorshipThis work was supported in part by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science and ICT (MSIT) of Korean Government under Grant 2023R1A2C1003783 and in part by the Institute of Information & Communications Technology Planning & Evaluation (IITP) under the Artificial Intelligence Convergence Innovation Human Resources Development grant funded by the Korean Government (MSIT) under Grant IITP-2023-RS-2023-00255968. The Associate Editor for this article was C. K. Sundarabalan.-
dc.language.isoeng-
dc.publisherInstitute of Electrical and Electronics Engineers Inc.-
dc.subject.mesh5g-
dc.subject.meshAdaptive resource allocations-
dc.subject.meshC-V2X-
dc.subject.meshDQN-
dc.subject.meshMultiple quality-
dc.subject.meshMultiple quality of service-
dc.subject.meshQuality-of-service-
dc.subject.meshService requirements-
dc.subject.meshStringents-
dc.subject.meshVehicular networks-
dc.titleMultiple QoS Enabled Intelligent Resource Management in Vehicle-to-Vehicle Communication-
dc.typeArticle-
dc.citation.endPage12094-
dc.citation.number9-
dc.citation.startPage12081-
dc.citation.titleIEEE Transactions on Intelligent Transportation Systems-
dc.citation.volume25-
dc.identifier.bibliographicCitationIEEE Transactions on Intelligent Transportation Systems, Vol.25 No.9, pp.12081-12094-
dc.identifier.doi10.1109/tits.2024.3365557-
dc.identifier.scopusid2-s2.0-85187020077-
dc.identifier.urlhttp://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979-
dc.subject.keyword5G-
dc.subject.keywordAdaptive resource allocation-
dc.subject.keywordC-V2X-
dc.subject.keywordDQN-
dc.subject.keywordmultiple QoS-
dc.subject.keywordvehicular networks-
dc.type.otherArticle-
dc.identifier.pissn1524-9050-
dc.description.isoafalse-
dc.subject.subareaAutomotive Engineering-
dc.subject.subareaMechanical Engineering-
dc.subject.subareaComputer Science Applications-
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Choi, Youngjune최영준
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