Show simple item record

dc.contributor.authorPérez Romero, Jordi
dc.contributor.authorSánchez González, Juan
dc.contributor.authorSallent Roig, José Oriol
dc.contributor.authorAgustí Comes, Ramon
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions
dc.date.accessioned2016-11-18T16:00:35Z
dc.date.issued2016
dc.identifier.citationPerez-Romero, J., Sanchez, J., Sallent, J., Agusti, R. On learning and exploiting time domain traffic patterns in cellular radio access networks. A: International Conference on Machine Learning and Data Mining in Pattern Recognition. "Machine learning and data mining in pattern recognition: 12th International Conference, MLDM 2016: New York, NY, USA, July 16-21, 2016: proceedings". New York, NY: Springer, 2016, p. 501-515.
dc.identifier.isbn978-3-319-41919-0
dc.identifier.urihttp://hdl.handle.net/2117/96855
dc.description.abstractThis paper presents a vision of how the different management procedures of future Fifth Generation (5G) wireless networks can be built upon the pillar of artificial intelligence concepts. After a general description of a cellular network and its management functionalities, highlighting the trends towards automatization, the paper focuses on the particular case of extracting knowledge about the time domain traffic pattern of the cells deployed by an operator. A general methodology for supervised classification of this traffic pattern is presented and it is particularized in two applicability use cases. The first use case addresses the reduction of energy consumption in the cellular network by automatically identifying cells that are candidates to be switched-off when they serve low traffic. The second use case focuses on the spectrum planning and identifies the cells whose capacity can be boosted through additional unlicensed spectrum. In both cases the outcomes of different classification tools are assessed. This capability to automatically classify cells according to some expert guidance is fundamental in future networks, where an operator deploys tenths of thousands of cells, so manual intervention of the expert is unfeasible.
dc.format.extent15 p.
dc.language.isoeng
dc.publisherSpringer
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Radiocomunicació i exploració electromagnètica::Comunicacions mòbils
dc.subject.lcshMobile communication systems
dc.subject.other5G mobile communication
dc.subject.otherCellular radio
dc.subject.otherEnergy consumption
dc.subject.otherLearning (artificial intelligence)
dc.subject.otherPattern classification
dc.subject.otherRadio access networks
dc.subject.otherRadio spectrum management
dc.subject.otherTelecommunication computing
dc.subject.otherTelecommunication network planning
dc.subject.otherTelecommunication traffic
dc.subject.otherUnlicensed spectrum
dc.subject.otherSpectrum planning
dc.subject.otherEnergy consumption reduction
dc.subject.otherTraffic pattern supervised classification
dc.subject.otherArtificial intelligence
dc.subject.other5G wireless networks
dc.subject.otherFifth generation wireless networks
dc.subject.otherCellular radio access networks
dc.subject.otherTime domain traffic patterns
dc.titleOn learning and exploiting time domain traffic patterns in cellular radio access networks
dc.typeConference report
dc.subject.lemacComunicacions mòbils, Sistemes de
dc.contributor.groupUniversitat Politècnica de Catalunya. GRCM - Grup de Recerca en Comunicacions Mòbils
dc.identifier.doi10.1007/978-3-319-41920-6_40
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://link.springer.com.recursos.biblioteca.upc.edu/chapter/10.1007%2F978-3-319-41920-6_40
dc.rights.accessRestricted access - publisher's policy
drac.iddocument19160782
dc.description.versionPostprint (published version)
dc.date.lift10000-01-01
upcommons.citation.authorPerez-Romero, J., Sanchez, J., Sallent, J., Agusti, R.
upcommons.citation.contributorInternational Conference on Machine Learning and Data Mining in Pattern Recognition
upcommons.citation.pubplaceNew York, NY
upcommons.citation.publishedtrue
upcommons.citation.publicationNameMachine learning and data mining in pattern recognition: 12th International Conference, MLDM 2016: New York, NY, USA, July 16-21, 2016: proceedings
upcommons.citation.startingPage501
upcommons.citation.endingPage515


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

Except where otherwise noted, content on this work is licensed under a Creative Commons license: Attribution-NonCommercial-NoDerivs 3.0 Spain