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Choose, not hoard: Information-to-model matching for Artificial Intelligence in O-RAN

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dc.affiliation.dptoUC3M. Departamento de Ingeniería Telemáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Network Technologieses
dc.contributor.authorMartín Pérez, Jorge
dc.contributor.authorMolner, Nuria
dc.contributor.authorMalandrino, Francesco
dc.contributor.authorBernardos Cano, Carlos Jesús
dc.contributor.authorOliva Delgado, Antonio de la
dc.contributor.authorGomez-Barquero, David
dc.contributor.funderEuropean Commissionen
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.date.accessioned2023-04-26T09:35:28Z
dc.date.available2023-04-26T09:35:28Z
dc.date.issued2023-04
dc.description.abstractOpen Radio Access Network (O-RAN) is an emerging paradigm, whereby virtualized network infrastructure elements from different vendors communicate via open, standardized interfaces. A key element therein is the RAN Intelligent Controller (RIC), an Artificial Intelligence (AI)-based controller. Traditionally, all data available in the network has been used to train a single AI model to be used at the RIC. This paper introduces, discusses, and evaluates the creation of multiple AI model instances at different RICs, leveraging information from some (or all) locations for their training. This brings about a flexible relationship between gNBs, the AI models used to control them, and the data such models are trained with. Experiments with real-world traces show how using multiple AI model instances that choose training data from specific locations improve the performance of traditional approaches following the hoarding strategy.en
dc.description.sponsorshipThis work was partially supported by the European Union's Horizon 2020 research and innovation program through the project iNGENIOUS under grant agreement No. 957216, and by the Spanish Ministry of Economic Aff airs and Digital Transformation and the European Union-NextGenerationEU through the UNICO 5G I+D 6G-EDGEDT and 6G-DATADRIVEN projects.en
dc.format.extent6
dc.identifier.bibliographicCitationMartin-Perez, J., Molner, N., Malandrino, F., Bernardos, C. J., De La Oliva, A., & Gomez-Barquero, D. (2023). Choose, not Hoard: Information-to-Model Matching for Artificial Intelligence in O-RAN. IEEE Communications Magazine, 61(4), 58-63.en
dc.identifier.doihttps://doi.org/10.1109/MCOM.003.2200401
dc.identifier.issn0163-6804
dc.identifier.publicationfirstpage58
dc.identifier.publicationissue4
dc.identifier.publicationlastpage63
dc.identifier.publicationtitleIEEE Communications Magazineen
dc.identifier.publicationvolume61
dc.identifier.urihttps://hdl.handle.net/10016/37203
dc.identifier.uxxiAR/0000031739
dc.language.isoeng
dc.publisherIEEE
dc.relation.projectIDGobierno de España. TSI-063000-2021-117es
dc.relation.projectIDGobierno de España. TSI-063000-2021-132es
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/957216
dc.rights© 2023 IEEE.es
dc.rights.accessRightsopen accessen
dc.subject.ecienciaTelecomunicacioneses
dc.subject.otherTrainingen
dc.subject.otherArtificial neural networksen
dc.subject.otherArtificial Intelligenceen
dc.subject.otherData modelsen
dc.subject.otherTraining dataen
dc.subject.otherServersen
dc.subject.otherBrain modelingen
dc.titleChoose, not hoard: Information-to-model matching for Artificial Intelligence in O-RANen
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
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