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dc.contributor.authorGuerra Gómez, Rolando
dc.contributor.authorRuiz Boqué, Sílvia
dc.contributor.authorGarcía Lozano, Mario
dc.contributor.authorOlmos Bonafé, Juan José
dc.contributor.otherUniversitat Politècnica de Catalunya. Doctorat en Teoria del Senyal i Comunicacions
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions
dc.date.accessioned2021-05-13T10:53:40Z
dc.date.issued2020
dc.identifier.citationGuerra, R. [et al.]. Machine-learning based traffic forecasting for resource management in C-RAN. A: European Conference on Networks and Communications. "2020 European Conference on Networks and Communications (EuCNC) - EuCNC 2020: European Conference on Networks and Communications, Dubrovnik, Croatia, June 15-18". Institute of Electrical and Electronics Engineers (IEEE), 2020, p. 1-5. ISBN 978-1-72814-355-2.
dc.identifier.isbn978-1-72814-355-2
dc.identifier.urihttp://hdl.handle.net/2117/345553
dc.description.abstractThe assumption of a fixed computational capacityat the Baseband Unit (BBU) pools in a Cloud Radio Access Network (C-RAN) deployment results in underutilized resourcesor unsatisfied users depending on traffic requirements. In thispaper a new strategy to predict the required resources based on Machine Learning techniques is proposed and analysed. SupportVector Machine (SVM), Time-Delay Neural Network (TDNN),and Long Short-Term Memory (LSTM) have been tested andcompared to select the best predicting approach. Instead of usinga regular synthetic scenario a realistic dense cell deployment overVienna city is used to validate the results. Authors show that theproposed solution reduces the unused resources average by 96 %
dc.description.sponsorshipThis work has been done under COST CA15104 IRACONEU project. It was supported in part by the Spanish ministryof science through the project RTI2018-099880-B-C32, with ERFD funds, and the Grant FPI-UPC provided by the UPC.
dc.format.extent5 p.
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.subjectÀrees temàtiques de la UPC::Enginyeria de la telecomunicació
dc.subject.lcshMachine learning
dc.subject.lcshSoftware-defined networking (Computer network technology)
dc.subject.othermachine learning
dc.subject.otherCloud Radio Access Network
dc.titleMachine-learning based traffic forecasting for resource management in C-RAN
dc.typeConference report
dc.subject.lemacAprenentatge automàtic
dc.subject.lemacXarxes definides per programari (Tecnologia de xarxes d'ordinadors)
dc.contributor.groupUniversitat Politècnica de Catalunya. WiComTec - Grup de recerca en Tecnologies i Comunicacions Sense Fils
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/9200958
dc.rights.accessRestricted access - publisher's policy
local.identifier.drac28703507
dc.description.versionPostprint (published version)
dc.date.lift10000-01-01
local.citation.authorGuerra, R.; Ruiz, S.; Garcia-Lozano, M.; Olmos, J.
local.citation.contributorEuropean Conference on Networks and Communications
local.citation.publicationName2020 European Conference on Networks and Communications (EuCNC) - EuCNC 2020: European Conference on Networks and Communications, Dubrovnik, Croatia, June 15-18
local.citation.startingPage1
local.citation.endingPage5


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