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dc.contributor.authorWang, Jing-Lingen_US
dc.contributor.authorLi, Yun-Rueien_US
dc.contributor.authorAdege, Abebe Belayen_US
dc.contributor.authorWang, Li-Chunen_US
dc.contributor.authorJeng, Shiann-Shiunen_US
dc.contributor.authorChen, Jen-Yeuen_US
dc.date.accessioned2019-05-02T00:26:47Z-
dc.date.available2019-05-02T00:26:47Z-
dc.date.issued2019-01-01en_US
dc.identifier.isbn978-1-5386-5553-5en_US
dc.identifier.issn2331-9852en_US
dc.identifier.urihttp://hdl.handle.net/11536/151717-
dc.description.abstractThis paper applies Machine Learning (ML) to predict the quality of Air-to-Ground (A2G) links performance for Unmanned Aerial Vehicles Base Stations (UAV-BSs) services. UAV-BSs can instantly identify the status of the current 3D wireless channel in an unknown environment without relying on previous statistical channel modeling. The proposed method that employs the unsupervised learning clustering technology applying to A2G channel modeling in 3D wireless communication scenarios. As environment changing, the proposed method can derive the 3D temporary channel model based on collected RSS data and analyzing. To evaluate the proposed method, the simulation data and measurement data are used to co-verify the performance. As the results shown, the RMSE of conventional statistical channel model and proposed temporary channel model are very similar. The similarity achieves about 91.8% both of the simulation and experimental environments to verify the accuracy and feasibility of our proposed method, and that provides more fast and effective of 3D channel modeling approach.en_US
dc.language.isoen_USen_US
dc.titleMachine Learning Based Rapid 3D Channel Modeling for UAV Communication Networksen_US
dc.typeProceedings Paperen_US
dc.identifier.journal2019 16TH IEEE ANNUAL CONSUMER COMMUNICATIONS & NETWORKING CONFERENCE (CCNC)en_US
dc.citation.spage0en_US
dc.citation.epage0en_US
dc.contributor.department電子工程學系及電子研究所zh_TW
dc.contributor.departmentDepartment of Electronics Engineering and Institute of Electronicsen_US
dc.identifier.wosnumberWOS:000462927900049en_US
dc.citation.woscount0en_US
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