Publication: AI-based Autonomous Control, Management, and Orchestration in 5G: from Standards to Algorithms
dc.affiliation.dpto | UC3M. Departamento de Ingeniería Telemática | es |
dc.affiliation.grupoinv | UC3M. Grupo de Investigación: Network Technologies | es |
dc.contributor.author | Bega, Dario | |
dc.contributor.author | Gramaglia, Marco | |
dc.contributor.author | Pérez, Ramón | |
dc.contributor.author | Fiore, Marco | |
dc.contributor.author | Banchs Roca, Albert | |
dc.contributor.author | Costa-Pérez, Xavier | |
dc.contributor.funder | European Commission | en |
dc.date.accessioned | 2021-01-25T13:26:39Z | |
dc.date.available | 2021-01-25T13:26:39Z | |
dc.date.issued | 2020-12-02 | |
dc.description.abstract | While the application of Artificial Intelligence (AI) to 5G networks has raised a 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 paper, 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 towards 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. | en |
dc.description.sponsorship | This work was supported by the H2020 5G-TOURS European project (Grant Agreement No. 856950). | en |
dc.format.extent | 7 | es |
dc.identifier.bibliographicCitation | IEEE network, 34(6), Pp. 14-20 | en |
dc.identifier.doi | https://doi.org/10.1109/MNET.001.2000047 | |
dc.identifier.issn | 0890-8044 | |
dc.identifier.issn | 1558-156X (online) | |
dc.identifier.publicationfirstpage | 14 | es |
dc.identifier.publicationissue | 6 | es |
dc.identifier.publicationlastpage | 20 | es |
dc.identifier.publicationtitle | IEEE NETWORK | en |
dc.identifier.publicationvolume | 34 | es |
dc.identifier.uri | https://hdl.handle.net/10016/31770 | |
dc.identifier.uxxi | AR/0000025931 | |
dc.language.iso | eng | en |
dc.publisher | IEEE | en |
dc.relation.projectID | info:eu-repo/grantAgreement/EC/H2020/856950/5G-TOURS | es |
dc.rights | © 2020 IEEE. | en |
dc.rights.accessRights | open access | en |
dc.subject.eciencia | Telecomunicaciones | es |
dc.subject.other | Artificial intelligence | en |
dc.subject.other | Forecasting | en |
dc.subject.other | Engines | en |
dc.subject.other | Prediction algorithms | en |
dc.subject.other | History | en |
dc.subject.other | Data analysis | en |
dc.subject.other | 3GPP | en |
dc.title | AI-based Autonomous Control, Management, and Orchestration in 5G: from Standards to Algorithms | en |
dc.type | research article | * |
dc.type.hasVersion | AM | * |
dspace.entity.type | Publication |
Files
Original bundle
1 - 1 of 1
Loading...
- Name:
- aibased_NETWORK_2020_ps.pdf
- Size:
- 325.21 KB
- Format:
- Adobe Portable Document Format