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Root cause analysis of low throughput situations using boosting algorithms and the TreeShap analysis

2022-08-25Conference object Open access
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dc.contributor.authorCilinio, M.
dc.contributor.authorDuarte, David
dc.contributor.authorVieira, Pedro
dc.contributor.authorQueluz, Maria Paula
dc.contributor.authorRodrigues, A.
dc.date.accessioned2023-05-16T08:10:13Z
dc.date.available2023-05-16T08:10:13Z
dc.date.issued2022-08-25
dc.description.abstractDetecting and diagnosing the root cause of failures in mobile networks is an increasingly demanding and time consuming task, given its technological growing complexity. This paper focuses on predicting and diagnosing low User Downlink (DL) Average Throughput situations, using supervised learning and the Tree Shapley Additive Explanations (SHAP) method. To fulfill this objective, Boosting classification models are used to predict a failure/non-failure binary label. The influence of each counter on the overall model’s predictive performance is performed based on the TreeSHAP method. From the implemen tation of this technique, it is possible to identify the main causes of low throughput, based on the analysis of the most critical counters in fault detection. Furthermore, from the identification of these counters, it is possible to define a system for diagnosing the most probable throughput degradation cause. The described methodology allowed not only to identify and quantify low throughput situations in a live network due to the occurrence of misadjusted configuration parameters, radio problems and network capacity problems, but also to outline a process for solving them.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationCILINIO, M.; [et al] – Root cause analysis of low throughput situations using boosting algorithms and the TreeShap analysis. In 2022 IEEE 95th Vehicular Technology Conference (VTC2022-Spring). Helsinki, Finland: IEEE, 2022. ISBN. 978-1-6654-8243-1. Pp. 1-5.pt_PT
dc.identifier.doi10.1109/VTC2022-Spring54318.2022.9860734pt_PT
dc.identifier.eissn2577-2465
dc.identifier.isbn978-1-6654-8243-1
dc.identifier.isbn978-1-6654-8244-8
dc.identifier.issn1090-3038
dc.identifier.urihttp://hdl.handle.net/10400.21/16035
dc.language.isoengpt_PT
dc.publisherIEEEpt_PT
dc.relation.publisherversionhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9860734pt_PT
dc.subjectMobile networkspt_PT
dc.subjectKPIspt_PT
dc.subjectPM indicatorspt_PT
dc.subjectPM indicatorspt_PT
dc.subjectBoosting classification modelspt_PT
dc.subjectTreeSHAPpt_PT
dc.titleRoot cause analysis of low throughput situations using boosting algorithms and the TreeShap analysispt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.citation.conferencePlace19-22 June 2022 - Helsinki, Finlandpt_PT
oaire.citation.endPage5pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.title2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring)pt_PT
person.familyNameVieira
person.familyNameQueluz
person.givenNamePedro
person.givenNameMaria Paula
person.identifier.ciencia-id071B-9A70-15B8
person.identifier.ciencia-idC210-DAC9-D03A
person.identifier.orcid0000-0003-0279-8741
person.identifier.orcid0000-0003-0266-4022
person.identifier.scopus-author-id7004567421
person.identifier.scopus-author-id6602528040
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT
relation.isAuthorOfPublication51ae3527-d4ea-46f4-b6c9-62c7b77ac728
relation.isAuthorOfPublication59d5af5d-9c01-4147-a3a9-c1ea74ef3a4d
relation.isAuthorOfPublication.latestForDiscovery59d5af5d-9c01-4147-a3a9-c1ea74ef3a4d

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