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dc.contributor.authorToril-Genoves, Matias 
dc.contributor.authorGijón, Carolina
dc.contributor.authorBejarano-Luque, Juan Luis
dc.contributor.authorLuna-Ramírez, Salvador 
dc.contributor.authorSánchez-Martín, Joaquín
dc.date.accessioned2022-09-14T10:20:54Z
dc.date.available2022-09-14T10:20:54Z
dc.date.created2022-09
dc.date.issued2022-09
dc.identifier.urihttps://hdl.handle.net/10630/24990
dc.description.abstractIn beyond 5G networks, geolocated radio information will play a fundamental role to drive self-management algorithms in a zero-touch paradigm. Minimization of Drive Test (MDT) functionality provides operators with geolocated network performance statistics and radio events. However, MDT traces contain important location errors due to energy saving modes, which requires filtering out wrong samples to guarantee an adequate performance of MDT-driven algorithms. In this context, supervised learning (SL) arises as a promising solution to automate the design of MDT filtering procedures compared to rule-based solutions. This work presents a SL-based method to filter MDT measurements in road scenarios by combining user mobility traces and land use maps in the absence of labeled real user mobility traces. Assessment is carried out over real MDT data collected in a live LTE network. Results show that the model performs better in measurements with positioning accuracy information.es_ES
dc.description.sponsorshipUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Teches_ES
dc.language.isospaes_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.subjectRedes de banda ancha - Congresoses_ES
dc.subjectAprendizaje automático (Inteligencia artificial) - Congresoses_ES
dc.subject.otherMDTes_ES
dc.subject.otherTrazases_ES
dc.subject.otherRedes 5Ges_ES
dc.subject.otherAlta movilidades_ES
dc.subject.otherAprendizaje supervisadoes_ES
dc.titleFiltrado de trazas MDT de alta movilidad mediante aprendizaje supervisadoes_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.centroE.T.S.I. Telecomunicaciónes_ES
dc.relation.eventtitleURSI 2022es_ES
dc.relation.eventplaceMálaga (España)es_ES
dc.relation.eventdateSeptiembre 2022es_ES


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