Full metadata record
DC FieldValueLanguage
dc.contributor.authorDeng, Changen_US
dc.contributor.authorXu, Wenjunen_US
dc.contributor.authorLee, Chia-Hanen_US
dc.contributor.authorGao, Huien_US
dc.contributor.authorXu, Wenboen_US
dc.contributor.authorFeng, Zhiyongen_US
dc.date.accessioned2020-10-05T02:01:27Z-
dc.date.available2020-10-05T02:01:27Z-
dc.date.issued2019-01-01en_US
dc.identifier.isbn978-1-7281-0962-6en_US
dc.identifier.issn2334-0983en_US
dc.identifier.urihttp://hdl.handle.net/11536/155237-
dc.description.abstractWe study an energy-efficient unmanned aerial vehicle (UAV) multicast system, in which ground terminals (GTs) requiring a file of common information (CI) are grouped and a UAV flies to each group to deliver the CI using minimum energy consumption. A machine learning (ML) empowered joint multicast grouping and UAV trajectory optimization framework is proposed to tackle the challenging joint optimization problem. In this framework, we first propose the compressed-feature regression and clustering machine learning ((CML)-M-2) for multicast grouping. A support vector regression (SVR) is trained with the silhouette coefficient, a one-dimensional compressed feature regarding the distribution of GTs, to efficiently determine the number of groups that guides the K-means clustering to approach the optimal multicast grouping. With the (CML)-M-2-enabled multicast grouping, we solve the UAV trajectory optimization problem by formulating an equivalent centroid-adjustable traveling salesman problem (CA-TSP). An efficient CA-TSP inspired iterative optimization algorithm is proposed for UAV trajectory planning. The proposed ML-empowered joint optimization framework, which integrates the offline (CML)-M-2-enabled multicast grouping and the online CA-TSP inspired UAV trajectory optimization, is shown to achieve excellent energy-saving performance.en_US
dc.language.isoen_USen_US
dc.subjectUnmanned aerial vehicle (UAV)en_US
dc.subjectmulticasten_US
dc.subjectenergy consumptionen_US
dc.subjectmulticast groupingen_US
dc.subjecttrajectory optimizationen_US
dc.titleEnergy Efficient UAV-Enabled Multicast Systems: Joint Grouping and Trajectory Optimizationen_US
dc.typeProceedings Paperen_US
dc.identifier.journal2019 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM)en_US
dc.citation.spage0en_US
dc.citation.epage0en_US
dc.contributor.department交大名義發表zh_TW
dc.contributor.departmentNational Chiao Tung Universityen_US
dc.identifier.wosnumberWOS:000552238603092en_US
dc.citation.woscount0en_US
Appears in Collections:Conferences Paper