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Can machine learning aid in delivering new use cases and scenarios in 5G?

 
: Buda, T.S.; Assem, H.; Xu, L.; Raz, D.; Margolin, U.; Rosensweig, E.; Lopez, D.R.; Corici, M.-I.; Smirnov, M.; Mullins, R.; Uryupina, O.; Mozo, A.; Ordozgoiti, B.; Martin, A.; Alloush, A.; O'Sullivan, P.; Ben Yahia, I.G.

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Badonnel, S.O. ; Institute of Electrical and Electronics Engineers -IEEE-; International Federation for Information Processing -IFIP-; IEEE Communications Society:
NOMS 2016, IEEE/IFIP Network Operations and Management Symposium. Proceedings : April 25-29, 2016, Istanbul, Turkey
Piscataway, NJ: IEEE, 2016
ISBN: 978-1-5090-0223-8
ISBN: 978-1-5090-0224-5
S.1279-1284
Network Operations and Management Symposium (NOMS) <2016, Istanbul>
Englisch
Konferenzbeitrag
Fraunhofer FOKUS ()

Abstract
5G represents the next generation of communication networks and services, and will bring a new set of use cases and scenarios. These in turn will address a new set of challenges from the network and service management perspective, such as network traffic and resource management, big data management and energy efficiency. Consequently, novel techniques and strategies are required to address these challenges in a smarter way. In this paper, we present the limitations of the current network and service management and describe in detail the challenges that 5G is expected to face from a management perspective. The main contribution of this paper is presenting a set of use cases and scenarios of 5G in which machine learning can aid in addressing their management challenges. It is expected that machine learning can provide a higher and more intelligent level of monitoring and management of networks and applications, improve operational efficiencies and facilitate the requirements of the future 5G network.

: http://publica.fraunhofer.de/dokumente/N-422253.html