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Machine Learning Methods for Slice Admission in 5G Networks
KTH, School of Electrical Engineering and Computer Science (EECS), Communication Systems, CoS, Optical Network Laboratory (ON Lab).
2019 (English)In: OECC/PSC 2019 - 24th OptoElectronics and Communications Conference/International Conference Photonics in Switching and Computing 2019, Institute of Electrical and Electronics Engineers (IEEE), 2019, article id 8817990Conference paper, Published paper (Refereed)
Abstract [en]

The paper discusses how the slice admission problem can be aided by machine learning strategies. Results show that both supervised and reinforcement learning might lead to profit maximization while containing losses due to performance degradation.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2019. article id 8817990
Keywords [en]
control and management, Data analytics for network control and management in optical core/data center networks, design, Optical core/metro/data-center network architecture, slice, virtualization
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-262588DOI: 10.23919/PS.2019.8817990Scopus ID: 2-s2.0-85072308945ISBN: 9784885523212 (print)OAI: oai:DiVA.org:kth-262588DiVA, id: diva2:1363064
Conference
24th OptoElectronics and Communications Conference/International Conference Photonics in Switching and Computing, OECC/PSC 2019; Fukuoka International Congress Center, Fukuoka; Japan; 7 July 2019 through 11 July 2019
Note

QC 20191022

Available from: 2019-10-22 Created: 2019-10-22 Last updated: 2019-10-22Bibliographically approved

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Raza, Muhammad Rehan

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