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dc.contributor.authorVillegas Carrasco, Javier
dc.contributor.authorFortes-Rodríguez, Sergio 
dc.contributor.authorBarco-Moreno, Raquel 
dc.date.accessioned2023-06-23T06:46:25Z
dc.date.available2023-06-23T06:46:25Z
dc.date.issued2023
dc.identifier.urihttps://hdl.handle.net/10630/27055
dc.description.abstractOver the past few years, indoor cellular deployments have been on the rise. These scenarios are characterized by their user density and fast-changing conditions, thus, being prone to failures. Moreover, the steady development of indoor and outdoor positioning techniques is expected to provide a reliable source of information. Thus, the availability of user location is being considered to be a key enabler to improve the resilience and performance of automatic failure management and optimization techniques. Taking this into consideration, the present work proposes a semi-supervised location-aware anomaly detection method for the management of failures such as cell outages and interference problems.es_ES
dc.description.sponsorshipUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.es_ES
dc.language.isoenges_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.subjectTelecomunicacioneses_ES
dc.subject.otherSemi-Supervisedes_ES
dc.subject.otherLocation-Awarees_ES
dc.subject.otherAnomaly Detectiones_ES
dc.subject.otherCellular networkses_ES
dc.titleA Semi-Supervised Location-Aware Anomaly Detection Method for Ultra-Dense Indoor Scenarios.es_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.centroE.T.S.I. Telecomunicaciónes_ES
dc.relation.eventtitle2023 EuCNC & 6G Summites_ES
dc.relation.eventplaceGotemburgoes_ES
dc.relation.eventdate06/06/2023es_ES


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