Proactive Dynamic Network Slicing with Deep Learning Based Short-Term Traffic Prediction for 5G Transport Network

dc.contributor.authorGuo, Qize
dc.contributor.authorGu, Rentao
dc.contributor.authorWang, Zihao
dc.contributor.authorZhao, Tianyi
dc.contributor.authorJi, Yuefeng
dc.contributor.authorKong, Jian
dc.contributor.authorGour, Riti
dc.contributor.authorJue, Jason P.
dc.contributor.utdAuthorKong, Jian
dc.contributor.utdAuthorGour, Riti
dc.contributor.utdAuthorJue, Jason P.
dc.date.accessioned2020-08-05T16:15:11Z
dc.date.available2020-08-05T16:15:11Z
dc.date.issued2019-03-03
dc.description.abstractWe propose a proactive dynamic network slicing scheme that utilizes a deep-learning based short-term traffic prediction approach for 5G transport networks. The demonstration shows utilization efficiency improvement from 46.33% to 71.53% under the evaluated scenario.
dc.description.departmentErik Jonsson School of Engineering and Computer Science
dc.description.sponsorshipNational Science and Technology Major Project (No.2017ZX03001016), NSFC (No.61871051) and Beijing Natural Science Foundation (Grand No. 4182040)
dc.identifier.bibliographicCitationGuo, Qize, Rentao Gu, Zihao Wang, Tianyi Zhao, et al. 2019 "Proactive Dynamic Network Slicing with Deep Learning Based Short-Term Traffic Prediction for 5G Transport Network." 2019 Optical Fiber Communication Conference and Exhibition: 1-3.
dc.identifier.issn978-1-9435-8053-8
dc.identifier.urihttps://hdl.handle.net/10735.1/8777
dc.language.isoen
dc.publisherIEEE
dc.relation2019 Optical Fiber Communication Conference and Exhibition
dc.rights©2019 OSA
dc.subjectEngineering
dc.subjectOptics
dc.subjectTelecommunication
dc.subject5G mobile communication systems
dc.titleProactive Dynamic Network Slicing with Deep Learning Based Short-Term Traffic Prediction for 5G Transport Network
dc.type.genrearticle

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