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Digital twins for next-generation mobile networks: Applications and solutions

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dc.affiliation.dptoUC3M. Departamento de IngenierĂ­a TelemĂ¡ticaes
dc.affiliation.grupoinvUC3M. Grupo de InvestigaciĂ³n: Network Technologieses
dc.contributor.authorApostolakis, Nikolaos
dc.contributor.authorChatzieleftheriou, Livia Elena
dc.contributor.authorBega, DarĂ­o
dc.contributor.authorGramaglia, Marco
dc.contributor.authorBanchs Roca, Albert
dc.contributor.funderEuropean Commissiones
dc.date.accessioned2023-11-28T12:38:55Z
dc.date.available2023-11-28T12:38:55Z
dc.date.issued2023-05-08
dc.description.abstractDigital Twins (DTs) create fully-synchronized virtual representations of real-world systems, which can serve as interactive counterparts for artificial intelligence (AI) and machine learning (ML) algorithms, and hold significant importance for the upcoming 6G mobile networks. In this paper, we argue that DTs can improve all phases of the intelligent networks' workflow, due to their adaptability and scalability properties that would allow them to transparently integrate new AI/ML algorithms faster, more scalably, and more precisely. Our contribution is two-fold: first, we propose three specific application scenarios of DT-enhanced network architectures in the context of 6G. Second, using open-source tools, we implement and evaluate in detail one of them. Our results demonstrate that our DT reflects the characteristics of the physical object, successfully and scalably twinning it, and adapting to changing contextual conditions.en
dc.description.sponsorshipThe work of University Carlos III of Madrid has been funded by the H2020 Project DAEMON (Grant Agreement No. 101017109), the Horizon Europe Project TrialsNet (Grant Agreement No. 101095871), and by the Spanish Ministry of Economic Affairs and Digital Transformation and the European Union-NextGenerationEU through the UNICO 5G I+D project 6G-CLARION.en
dc.description.statusPublicadoes
dc.format.extent6
dc.identifier.bibliographicCitationIEEE Communications Magazine, (2023), 61(11), pp.: 80-86.en
dc.identifier.doihttps://doi.org/10.1109/MCOM.001.2200854
dc.identifier.issn0163-6804
dc.identifier.publicationfirstpage80
dc.identifier.publicationissue11
dc.identifier.publicationlastpage86
dc.identifier.publicationtitleIEEE COMMUNICATIONS MAGAZINEen
dc.identifier.publicationvolume61
dc.identifier.urihttps://hdl.handle.net/10016/38982
dc.identifier.uxxiAR/0000032968
dc.language.isoengen
dc.publisherIEEEen
dc.relation.projectIDinfo:eu-repo/grantAgreement/H2020/101017109/DAEMONen
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/101095871/TrialsNeten
dc.rights© 2023 IEEE.en
dc.rights.accessRightsopen accessen
dc.subject.ecienciaTelecomunicacioneses
dc.subject.otherDigital twinen
dc.subject.otherRadio access networksen
dc.subject.otherVranen
dc.subject.otherMachine learningen
dc.titleDigital twins for next-generation mobile networks: Applications and solutionsen
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
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