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Joint Optimization of Trajectory and Resource Allocation for Multi-UAV-Enabled Wireless-Powered Communication Networks

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dc.contributor.authorKim, Chaeyeon-
dc.contributor.authorChoi, Hyun-Ho-
dc.contributor.authorLee, Kisong-
dc.date.accessioned2024-08-08T11:31:51Z-
dc.date.available2024-08-08T11:31:51Z-
dc.date.issued2024-09-
dc.identifier.issn0090-6778-
dc.identifier.issn1558-0857-
dc.identifier.urihttps://scholarworks.dongguk.edu/handle/sw.dongguk/21821-
dc.description.abstractThis paper considers a multiple unmanned aerial vehicle (UAV)-enabled wireless powered communication network (WPCN). In this WPCN, UAVs broadcast radio frequency (RF) signals to facilitate a wireless power transfer (WPT) during the downlink phase, and ground nodes (GNs) harvest energy from these RF signals and transmit data to their respective UAVs in the uplink phase. To maximize the minimum uplink throughput of GNs, we jointly optimize the scheduling, transmit power of GNs, and trajectory of UAVs, while satisfying the energy neutrality of GNs and the mobility constraints of UAVs. To solve this non-convex optimization problem, we apply a successive convex approximation to divide the original problem into subproblems and make each of them convex for each optimization variable. Subsequently, we propose an iterative algorithm based on a block coordinate descent technique and efficiently find the optimal solution for each convex subproblem. The simulation result reveals that resource allocation and the trajectory of UAVs are strongly influenced by the interference level within the network. Furthermore, the result verifies that the proposed optimization approach significantly outperforms existing baseline schemes by properly coordinating co-channel and cross-link interferences between distinct UAV networks. IEEE-
dc.format.extent13-
dc.language영어-
dc.language.isoENG-
dc.publisherIEEE-
dc.titleJoint Optimization of Trajectory and Resource Allocation for Multi-UAV-Enabled Wireless-Powered Communication Networks-
dc.typeArticle-
dc.publisher.location미국-
dc.identifier.doi10.1109/TCOMM.2024.3383113-
dc.identifier.scopusid2-s2.0-85189370468-
dc.identifier.wosid001319557300028-
dc.identifier.bibliographicCitationIEEE Transactions on Communications, v.72, no.9, pp 5752 - 5764-
dc.citation.titleIEEE Transactions on Communications-
dc.citation.volume72-
dc.citation.number9-
dc.citation.startPage5752-
dc.citation.endPage5764-
dc.type.docTypeArticle-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaEngineering-
dc.relation.journalResearchAreaTelecommunications-
dc.relation.journalWebOfScienceCategoryEngineering, Electrical & Electronic-
dc.relation.journalWebOfScienceCategoryTelecommunications-
dc.subject.keywordAuthorAutonomous aerial vehicles-
dc.subject.keywordAuthorconvex optimization-
dc.subject.keywordAuthorinterference coordination-
dc.subject.keywordAuthorOptimization-
dc.subject.keywordAuthorResource management-
dc.subject.keywordAuthorThroughput-
dc.subject.keywordAuthorTrajectory-
dc.subject.keywordAuthortrajectory optimization-
dc.subject.keywordAuthorUnmanned aerial vehicle-
dc.subject.keywordAuthorUplink-
dc.subject.keywordAuthorWireless communication-
dc.subject.keywordAuthorwireless power transfer-
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