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dc.contributor.authorBega, Dario 
dc.contributor.authorGramaglia, Marco 
dc.contributor.authorPérez, Ramón
dc.contributor.authorFiore, Marco 
dc.contributor.authorBanchs, Albert 
dc.contributor.authorCosta-Perez, Xavier
dc.date.accessioned2021-07-13T09:47:24Z
dc.date.available2021-07-13T09:47:24Z
dc.date.issued2020-12-02
dc.identifier.urihttp://hdl.handle.net/20.500.12761/928
dc.description.abstractWhile the application of artificial intelligence (Ai) to 5G networks has raised strong interest, standard solutions to bring Ai into 5G systems are still in their infancy and have a long way to go before they can be used to build an operational system. in this article, we contribute to bridging the gap between standards and a working solution by defining a framework that brings together the relevant standard specifications and complements them with additional building blocks. We populate this framework with concrete Ai-based algorithms that serve different purposes toward developing a fully operational system. We evaluate the performance resulting from applying our framework to control, management, and orchestration functions, showing the benefits that Ai can bring to 5G systems.
dc.language.isoeng
dc.publisherIEEE
dc.titleAI-Based Autonomous Control, Management, and Orchestration in 5G: From Standards to Algorithmsen
dc.typemagazine
dc.journal.titleIEEE Network
dc.rights.accessRightsopen access
dc.volume.number34
dc.issue.number6
dc.identifier.doi10.1109/MNET.001.2000047
dc.page.final20
dc.page.initial14
dc.description.statuspub
dc.eprint.idhttp://eprints.networks.imdea.org/id/eprint/2280


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