Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/32666
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dc.contributor.authorZhang, J-
dc.contributor.authorJi, M-
dc.contributor.authorWu, Q-
dc.contributor.authorFan, P-
dc.contributor.authorWang, K-
dc.contributor.authorChen, W-
dc.date.accessioned2026-01-16T18:20:42Z-
dc.date.available2026-01-16T18:20:42Z-
dc.date.issued2025-12-25-
dc.identifierORCiD: Maoxin Ji https://orcid.org/0009-0000-8179-1710-
dc.identifierORCiD: Qiong Wu https://orcid.org/0000-0002-4899-1718-
dc.identifierORCiD: Pingyi Fan https://orcid.org/0000-0002-0658-6079-
dc.identifierORCiD: Kezhi Wang https://orcid.org/0000-0001-8602-0800-
dc.identifierORCiD: Wen Chen https://orcid.org/0000-0003-2133-8679-
dc.identifier.citationZhang, J. et al. (2025) 'Semantic-Aware Cooperative Communication and Computation Framework in Vehicular Networks', IEEE Networking Letters, 0 (early access), pp. 1 - 5. doi: 10.1109/LNET.2025.3648419.en_US
dc.identifier.urihttps://bura.brunel.ac.uk/handle/2438/32666-
dc.description.abstractSemantic Communication (SC) combined with Vehicular edge computing (VEC) provides an efficient edge task processing paradigm for Internet of Vehicles (IoV). Focusing on highway scenarios, this paper proposes a Tripartite Cooperative Semantic Communication (TCSC) framework, which enables Vehicle Users (VUs) to perform semantic task offloading via Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) communications. Considering task latency and the number of semantic symbols, the framework constructs a Mixed-Integer Nonlinear Programming (MINLP) problem, which is transformed into two subproblems. First, we innovatively propose a multi-agent proximal policy optimization task offloading optimization method based on parametric distribution noise (MAPPO-PDN) to solve the optimization problem of the number of semantic symbols; second, linear programming (LP) is used to optimize the offloading ratio. Simulations show that performance of this scheme is superior to that of other algorithms.en_US
dc.description.sponsorshipThis work was supported in part by Jiangxi Province Science and Technology Development Programme under Grant 20242BCC32016; in part by the National Natural Science Foundation of China under Grant 61701197; in part by Basic Research Program of Jiangsu under Grant BK20252084; in part by the National Key Research and Development Program of China under Grant 2021YFA1000500(4) and in part by the 111 Project under Grant B23008.en_US
dc.format.extent1 - 5-
dc.format.mediumElectronic-
dc.languageEnglish-
dc.language.isoen_USen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.rightsCreative Commons Attribution 4.0 International-
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/-
dc.subjectsemantic communicationen_US
dc.subjectvehicle edge computingen_US
dc.subjecttask offloadingen_US
dc.subjectmulti-agent reinforcement learningen_US
dc.titleSemantic-Aware Cooperative Communication and Computation Framework in Vehicular Networksen_US
dc.typeArticleen_US
dc.identifier.doihttps://doi.org/10.1109/LNET.2025.3648419-
dc.relation.isPartOfIEEE Networking Letters-
pubs.issue0-
pubs.publication-statusPublished-
pubs.volume00-
dc.identifier.eissn2576-3156-
dc.rights.licensehttps://creativecommons.org/licenses/by/4.0/legalcode.en-
dc.rights.holderThe Author(s)-
dc.contributor.orcidJi, Maoxin [0009-0000-8179-1710]-
dc.contributor.orcidWu, Qiong [0000-0002-4899-1718]-
dc.contributor.orcidFan, Pingyi [0000-0002-0658-6079]-
dc.contributor.orcidWang, Kezhi [0000-0001-8602-0800]-
dc.contributor.orcidChen, Wen [0000-0003-2133-8679]-
Appears in Collections:Dept of Computer Science Research Papers

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