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AI-Based Autonomous Control, Management, and Orchestration in 5G: From Standards to Algorithms
dc.contributor.author | Bega, Dario | |
dc.contributor.author | Gramaglia, Marco | |
dc.contributor.author | Pérez, Ramón | |
dc.contributor.author | Fiore, Marco | |
dc.contributor.author | Banchs, Albert | |
dc.contributor.author | Costa-Perez, Xavier | |
dc.date.accessioned | 2021-07-13T09:47:24Z | |
dc.date.available | 2021-07-13T09:47:24Z | |
dc.date.issued | 2020-12-02 | |
dc.identifier.uri | http://hdl.handle.net/20.500.12761/928 | |
dc.description.abstract | While 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.iso | eng | |
dc.publisher | IEEE | |
dc.title | AI-Based Autonomous Control, Management, and Orchestration in 5G: From Standards to Algorithms | en |
dc.type | magazine | |
dc.journal.title | IEEE Network | |
dc.rights.accessRights | open access | |
dc.volume.number | 34 | |
dc.issue.number | 6 | |
dc.identifier.doi | 10.1109/MNET.001.2000047 | |
dc.page.final | 20 | |
dc.page.initial | 14 | |
dc.description.status | pub | |
dc.eprint.id | http://eprints.networks.imdea.org/id/eprint/2280 |