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dc.contributor.authorMoysen Cortes, Jessica
dc.contributor.authorGiupponi, Lorenza
dc.contributor.authorMangues Bafalluy, Josep
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions
dc.date.accessioned2018-04-03T09:37:54Z
dc.date.issued2016
dc.identifier.citationMoysen, J., Giupponi, L., J. M. On the potential of ensemble regression techniques for future mobile network planning. A: IEEE Symposium on Computers and Communications. "2016 IEEE Symposium on Computers and Communication (ISCC) took place 27 June-1 July 2016 in Messina, Italy". Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 1-7.
dc.identifier.isbn978-1-5090-0679-3
dc.identifier.urihttp://hdl.handle.net/2117/115876
dc.description.abstractPlanning of current and future mobile networks is becoming increasingly complex due to the heterogeneity of deployments, which feature not only macrocells, but also an underlying layer of small cells whose deployment is not fully under the control of the operator. In this paper, we focus on selecting the most appropriate Quality of Service (QoS) prediction techniques for assisting network operators in planning future dense deployments. We propose to use machine learning as a tool to extract the relevant information from the huge amount of data generated in current 4G and future 5G networks during normal operation, which is then used to appropriately plan networks. In particular, we focus on radio measurements to develop correlative statistical models with the purpose of improving QoS-based network planning. In this direction, we combine multiple learners by building ensemble methods and use them to do regression in a reduced space rather than in the original one. We then compare the QoS prediction accuracy of various approaches that take as input the 3GPP Minimization of Drive Tests (MDT) measurements collected throughout a heterogeneous network and analyse their trade-offs. We also explain how the collected data is processed and used to predict QoS expressed in terms of Physical Resource Block (PRB)/ Megabit (MB) transmitted. This metric was selected because of the interest it may have for operators in planning, since it relates lower layer resources with their impact in terms of QoS up in the protocol stack, hence closer to the end-user.
dc.format.extent7 p.
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.subjectÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Radiocomunicació i exploració electromagnètica::Comunicacions mòbils
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
dc.subject.lcshMachine learning
dc.subject.lcshNetwork analysis (Planning)
dc.subject.lcshMobile communication systems
dc.subject.otherMachine Learning
dc.subject.otherBig Data
dc.subject.otherQuality of Service
dc.subject.otherPrediction
dc.subject.otherNetwork planning
dc.subject.otherMinimization of Drive Tests
dc.titleOn the potential of ensemble regression techniques for future mobile network planning
dc.typeConference report
dc.subject.lemacAprenentatge automàtic
dc.subject.lemacAnàlisi de xarxes (Planificació)
dc.subject.lemacComunicacions mòbils, Sistemes de
dc.contributor.groupUniversitat Politècnica de Catalunya. WiComTec - Grup de recerca en Tecnologies i Comunicacions Sense Fils
dc.identifier.doi10.1109/ISCC.2016.7543784
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://ieeexplore.ieee.org/document/7543784
dc.rights.accessRestricted access - publisher's policy
local.identifier.drac21880245
dc.description.versionPostprint (published version)
dc.date.lift10000-01-01
local.citation.authorMoysen, J.; Giupponi, L.; Mangues-Bafalluy, Josep
local.citation.contributorIEEE Symposium on Computers and Communications
local.citation.publicationName2016 IEEE Symposium on Computers and Communication (ISCC) took place 27 June-1 July 2016 in Messina, Italy
local.citation.startingPage1
local.citation.endingPage7


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