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dc.contributor.authorFortes, Sergio
dc.contributor.authorMuñoz Luengo, Pablo 
dc.date.accessioned2020-11-23T13:11:58Z
dc.date.available2020-11-23T13:11:58Z
dc.date.issued2020
dc.identifier.citationFortes S, Muñoz P, Serrano I, Barco R. Transform-Based Multiresolution Decomposition for Degradation Detection in Cellular Networks. Sensors. 2020; 20(19):5645. [https://doi.org/10.3390/s20195645]es_ES
dc.identifier.urihttp://hdl.handle.net/10481/64453
dc.description.abstractAnomaly detection in the performance of the huge number of elements that are part of cellular networks (base stations, core entities, and user equipment) is one of the most time consuming and key activities for supporting failure management procedures and ensuring the required performance of the telecommunication services. This activity originally relied on direct human inspection of cellular metrics (counters, key performance indicators, etc.). Currently, degradation detection procedures have experienced an evolution towards the use of automatic mechanisms of statistical analysis and machine learning. However, pre-existent solutions typically rely on the manual definition of the values to be considered abnormal or on large sets of labeled data, highly reducing their performance in the presence of long-term trends in the metrics or previously unknown patterns of degradation. In this field, the present work proposes a novel application of transform-based analysis, using wavelet transform, for the detection and study of network degradations. The proposed system is tested using cell-level metrics obtained from a real-world LTE cellular network, showing its capabilities to detect and characterize anomalies of different patterns and in the presence of varied temporal trends. This is performed without the need for manually establishing normality thresholds and taking advantage of wavelet transform capabilities to separate the metrics in multiple time-frequency components. Our results show how direct statistical analysis of these components allows for a successful detection of anomalies beyond the capabilities of detection of previous methods.es_ES
dc.description.sponsorshipOptimi-Ericssones_ES
dc.description.sponsorshipJunta de Andaluciaes_ES
dc.description.sponsorshipEuropean Union (EU) 59288es_ES
dc.description.sponsorshipProyecto de Investigacion de Excelencia P12-TIC-2905es_ES
dc.description.sponsorshipproject IDADE-5G UMA18-FEDERJA-201es_ES
dc.description.sponsorshipEuropean Union (EU) ICT-760809es_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.rightsAtribución 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectCellular managementes_ES
dc.subjectFailure detectiones_ES
dc.subjectSelf-healinges_ES
dc.subjectTransform-basedes_ES
dc.subjectWaveletes_ES
dc.titleTransform-Based Multiresolution Decomposition for Degradation Detection in Cellular Networkses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doi10.3390/s20195645


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Atribución 3.0 España
Except where otherwise noted, this item's license is described as Atribución 3.0 España