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Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/27235
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dc.contributor.authorJiang, F-
dc.contributor.authorPeng, Y-
dc.contributor.authorWang, K-
dc.contributor.authorLi, D-
dc.contributor.authorYang, K-
dc.date.accessioned2023-09-22T17:17:26Z-
dc.date.available2023-09-22T17:17:26Z-
dc.date.issued2023-09-19-
dc.identifierORCID iD: Feibo Jiang https://orcid.org/0000-0002-0235-0253-
dc.identifierORCID iD: Yubo Peng https://orcid.org/0000-0001-9684-2971-
dc.identifierORCID iD: Kezhi Wang https://orcid.org/0000-0001-8602-0800-
dc.identifierORCID iD: Li Dong https://orcid.org/0000-0002-0127-8480-
dc.identifierORCID iD: Kun Yang https://orcid.org/0000-0002-6782-6689-
dc.identifier.citationJiang, F. et al. (2023) 'MARS: a DRL-based Multi-task Resource Scheduling Framework for UAV with IRS-assisted Mobile Edge Computing System', IEEE Transactions on Cloud Computing, 11 (4), pp. 3700 - 3712. doi: 10.1109/TCC.2023.3307582.en_US
dc.identifier.urihttps://bura.brunel.ac.uk/handle/2438/27235-
dc.description.sponsorship10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 41604117 and 41904127); Open Project of Xiangjiang Laboratory (Grant Number: 22XJ03011); Scientific Research Fund of Hunan Provincial Education Department (Grant Number: 22B0663).en_US
dc.format.extent3700 - 3712-
dc.format.mediumElectronic-
dc.language.isoen_USen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.rightsCopyright © 2023 Institute of Electrical and Electronics Engineers (IEEE). Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works by sending a request to pubs-permissions@ieee.org. See: https://www.ieee.org/publications/rights/rights-policies.html-
dc.rights.urihttps://www.ieee.org/publications/rights/rights-policies.html-
dc.subjectmobile edge computing (MEC)en_US
dc.subjectintelligent re-flecting surface (IRS)en_US
dc.subjectunmanned aerial vehicle (UAV)en_US
dc.subjectdeep reinforcement learning (DRL)en_US
dc.subjectresource schedulingen_US
dc.titleMARS: a DRL-based Multi-task Resource Scheduling Framework for UAV with IRS-assisted Mobile Edge Computing Systemen_US
dc.typeArticleen_US
dc.identifier.doihttps://doi.org.10.1109/TCC.2023.3307582-
dc.relation.isPartOfIEEE Transactions on Cloud Computing-
pubs.issue4-
pubs.publication-statusPublished online-
pubs.volume11-
dc.identifier.eissn2168-7161-
dc.rights.holderInstitute of Electrical and Electronics Engineers (IEEE)-
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