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dc.contributor.authorMoysen Cortes, Jessica
dc.contributor.authorGarcía Lozano, Mario
dc.contributor.authorGiupponi, Lorenza
dc.contributor.authorRuiz Boqué, Sílvia
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions
dc.date.accessioned2018-09-04T08:52:08Z
dc.date.available2018-09-04T08:52:08Z
dc.date.issued2018-05-01
dc.identifier.citationMoysen, J., Garcia-Lozano, M., Giupponi, L., Ruiz, S. Conflict resolution in mobile networks: a self-coordination framework based on non-dominated solutions and machine learning for data analytics [Application notes]. "IEEE computational intelligence magazine", 1 Maig 2018, vol. 13, núm. 2, p. 52-64.
dc.identifier.issn1556-603X
dc.identifier.urihttp://hdl.handle.net/2117/120808
dc.description©2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.description.abstractSelf-organizing network (SON) is a well-known term used to describe an autonomous cellular network. SON functionalities aim at improving network operational tasks through the capability to configure, optimize and heal itself. However, as the deployment of independent SON functions increases, the number of dependencies between them also grows. This work proposes a tool for efficient conflict resolution based on network performance predictions. Unlike other state-of-theart solutions, the proposed self-coordination framework guarantees the right selection of network operation even if conflicting SON functions are running in parallel. This self-coordination is based on the history of network measurements, which helps to optimize conflicting objectives with low computational complexity. To do this, machine learning (ML) is used to build a predictive model, and then we solve the SON conflict by optimizing more than one objective function simultaneously. Without loss of generality, we present an analysis of how the proposed scheme provides a solution to deal with the potential conflicts between two of the most important SON functions in the context of mobility, namely mobility load balancing (MLB) and mobility robustness optimization (MRO), which require the updating of the same set of handover parameters. The proposed scheme allows fast performance evaluations when the optimization is running. This is done by shifting the complexity to the creation of a prediction model that uses historical data and that allows to anticipate the network performance. The simulation results demonstrate the ability of the proposed scheme to find a compromise among conflicting actions, and show it is possible to improve the overall system throughput.
dc.format.extent13 p.
dc.language.isoeng
dc.subjectÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Radiocomunicació i exploració electromagnètica::Comunicacions mòbils
dc.subject.lcshMobile communication systems
dc.subject.lcshBig data
dc.subject.otherOptimization
dc.subject.otherHandover
dc.subject.otherPredictive models
dc.subject.otherSelf-organizing networks
dc.subject.otherComplexity theory
dc.subject.otherBig Data
dc.subject.otherCellular networks
dc.subject.otherComputaional modeling
dc.subject.otherPredictive models
dc.titleConflict resolution in mobile networks: a self-coordination framework based on non-dominated solutions and machine learning for data analytics [Application notes]
dc.typeArticle
dc.subject.lemacComunicacions mòbils, sistemes de
dc.subject.lemacDades massives
dc.contributor.groupUniversitat Politècnica de Catalunya. WiComTec - Grup de recerca en Tecnologies i Comunicacions Sense Fils
dc.identifier.doi10.1109/MCI.2018.2807038
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/8335833/
dc.rights.accessOpen Access
drac.iddocument23182998
dc.description.versionPostprint (author's final draft)
upcommons.citation.authorMoysen, J., Garcia-Lozano, M., Giupponi, L., Ruiz, S.
upcommons.citation.publishedtrue
upcommons.citation.publicationNameIEEE computational intelligence magazine
upcommons.citation.volume13
upcommons.citation.number2
upcommons.citation.startingPage52
upcommons.citation.endingPage64


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