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Demo: vrAIn proof-of-concept: a deep learning approach for virtualized RAN resource control

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dc.affiliation.dptoUC3M. Departamento de Ingeniería Telemáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Network Technologieses
dc.contributor.authorAyala Romero, José A.
dc.contributor.authorGarcia Saavedra, Andres
dc.contributor.authorGramaglia, Marco
dc.contributor.authorCosta-Pérez, Xavier
dc.contributor.authorBanchs Roca, Albert
dc.contributor.authorAlcaraz, Juan J.
dc.contributor.funderEuropean Commissionen
dc.date.accessioned2019-10-31T16:46:52Z
dc.date.available2019-10-31T16:46:52Z
dc.date.issued2019-08-05
dc.descriptionProceeding of: 25th Annual International Conference on Mobile Computing and Networking (MobiCom'19), October 21-25, 2019, Los Cabos, Mexico.en
dc.description.abstractWhile the application of the NFV paradigm into the network is proceeding full steam ahead, there is still one last milestone to be achieved in this context: the virtualization of the radio access network (vRAN). Due to the very complex dependency between the radio conditions and the computing resources needed to provide the baseband processing functionality, attaining an efficient resource control is particularly challenging. In this demonstration, we will showcase vrAIn, a vRAN dynamic resource controller that employs deep reinforcement learning to perform resource assignment decisions. vrAIn, which is implemented using an open-source LTE stack over a Linux platform, can achieve substantial savings in the used CPU resources while maintaining the target QoS for the attached terminals and maximizing throughput when there is a deficit of computational capacity.en
dc.description.sponsorshipThe work of University Carlos III of Madrid was supported by H2020 5G-MoNArch project (grant agreement no. 761445) and H2020 5G-TOURS project (grant agreement no. 856950). The work of NEC Laboratories Europe was supported by H2020 5G-TRANSFORMER project (grant agreement no. 761536) and 5GROWTH project (grant agreement no. 856709). The work of University of Cartagena was supported by Grant AEI/FEDER TEC2016-76465-C2-1-R (AIM) and Grant FPU14/03701.en
dc.format.extent3
dc.identifier.bibliographicCitationProceedings of the 25th Annual International Conference on Mobile Computing and Networking (MobiCom'19). New York: ACM, cop. 2019. Article nº 59, [3] pp.en
dc.identifier.doihttps://doi.org/10.1145/3300061.3343370
dc.identifier.isbn978-1-4503-6169-9
dc.identifier.publicationtitleProceedings of the 25th Annual International Conference on Mobile Computing and Networking (MobiCom'19)en
dc.identifier.urihttps://hdl.handle.net/10016/29104
dc.identifier.uxxiCC/0000029939
dc.language.isoengen
dc.publisherAssociation for Computing Machineryen
dc.relation.eventdateOctober 21-25, 2019.en
dc.relation.eventnumber25
dc.relation.eventplaceLos Cabos, Méxicoen
dc.relation.eventtitleAnnual International Conference on Mobile Computing and Networking (MobiCom'19)en
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/761445/5G-MoNArchen
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/856950/5G-TOURSen
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/761536/5G-TRANSFORMERen
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/856709/5GROWTHen
dc.rights© 2019 Copyright held by the owner/author(s).en
dc.rights.accessRightsopen accessen
dc.subject.ecienciaTelecomunicacionesen
dc.subject.otherDeep learningen
dc.subject.otherVirtualized RANen
dc.subject.otherPrototypesen
dc.subject.otherNetwork measurementen
dc.subject.otherProgrammable networksen
dc.subject.otherMobile networksen
dc.titleDemo: vrAIn proof-of-concept: a deep learning approach for virtualized RAN resource controlen
dc.typeconference presentation*
dc.type.hasVersionAM*
dspace.entity.typePublication
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