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dc.contributor.authorVy, Le Luongen_US
dc.contributor.authorTung, Li-Pingen_US
dc.contributor.authorLin, Bao-Shuh Paulen_US
dc.date.accessioned2018-08-21T05:56:57Z-
dc.date.available2018-08-21T05:56:57Z-
dc.date.issued2017-01-01en_US
dc.identifier.urihttp://hdl.handle.net/11536/146856-
dc.description.abstractHandover (HO), as a key aspect of mobility management, plays an important role in improving network quality and mobility performance in mobile networks. Especially, in 5G networks, heterogeneous networks (HetNets) deployment of macro cells and small cells, and the deployment of ultra-dense networks (UDNs) make HO management become more challenging. Besides, the understanding of HO behavior in a cell is quite limited in existing studies, thus the forecasting HO for an individual cell is complicated, even impossible. This challenge led the authors to propose a practical process for managing and forecasting HO for a huge number of cells, based on machine-learning (ML) algorithms and big data. Moreover, based on HO forecasting, the authors also propose an approach to detect any abnormal HO in cells. The performance of the proposed approaches was evaluated by applying it to a real dataset that collected HO KPI of more than 6000 cells of a real network during the years, 2016 and 2017. The results show that the study was successful in identifying, separating HO behavior, forecasting the future number of HO attempts, and detecting abnormal HO behaviors of cells.en_US
dc.language.isoen_USen_US
dc.subjectkey performance indicators (KPIs)en_US
dc.subjectMachine Learningen_US
dc.subjectSONen_US
dc.subject5Gen_US
dc.subjectdrive testen_US
dc.subjecthandoveren_US
dc.subjectbig dataen_US
dc.titleBig Data and Machine Learning Driven Handover Management and Forecastingen_US
dc.typeProceedings Paperen_US
dc.identifier.journal2017 IEEE CONFERENCE ON STANDARDS FOR COMMUNICATIONS AND NETWORKING (CSCN)en_US
dc.citation.spage214en_US
dc.citation.epage219en_US
dc.contributor.department材料科學與工程學系zh_TW
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Materials Science and Engineeringen_US
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000417425200036en_US
Appears in Collections:Conferences Paper