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dc.contributor.authorBega, Dario 
dc.contributor.authorGramaglia, Marco 
dc.contributor.authorGarcia-Saavedra, Andres
dc.contributor.authorFiore, Marco 
dc.contributor.authorBanchs, Albert 
dc.contributor.authorCosta-Perez, Xavier
dc.date.accessioned2021-07-13T09:43:28Z
dc.date.available2021-07-13T09:43:28Z
dc.date.issued2020-06
dc.identifier.urihttp://hdl.handle.net/20.500.12761/843
dc.description.abstractNetwork slicing is an emerging paradigm in mobile networks that leverages Network Function Virtualization (NFV) to enable the instantiation of multiple virtual networks –named slices– over the same physical network infrastructure. The operator can allocate to each slice dedicated resources and customized functions that allow meeting the highly heterogeneous and stringent requirements of modern mobile services. Managing functions and resources under network slicing is a challenging task that requires making efficient decisions at all network levels, in some cases even in real-time, which can be achieved by integrating artificial intelligence (AI) in the network. We outline a general framework for AI-based network slice management, introducing AI in the different phases of the slice lifecycle, from admission control to dynamic resource allocation in the network core and at the radio access. A sensible use of AI for network slicing results in strong benefits for the operator, with expected performance gains between 25% and 80% in representative case studies.
dc.language.isoeng
dc.titleNetwork Slicing Meets Artificial Intelligence: an AI-based Framework for Slice Managementen
dc.typemagazine
dc.journal.titleIEEE Communications Magazine
dc.type.hasVersionAM
dc.rights.accessRightsopen access
dc.volume.number58
dc.issue.number6
dc.identifier.doi10.1109/MCOM.001.1900653
dc.description.statuspub
dc.eprint.idhttp://eprints.networks.imdea.org/id/eprint/2177


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