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On the Use of Spatial Graphs for Performance Degradation Root-Cause Analysis Toward Self-Healing Mobile Networks

2024-02-01Journal article Open access
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dc.contributor.authorMata, Luís
dc.contributor.authorSousa, Marco
dc.contributor.authorVieira, Pedro
dc.contributor.authorQueluz, Maria Paula
dc.contributor.authorRodrigues, António
dc.date.accessioned2024-11-11T15:43:41Z
dc.date.available2024-11-11T15:43:41Z
dc.date.issued2024-02-01
dc.description.abstractOn the road to the sixth generation of cellular networks (6G), the need to ensure a sustainable usage of natural resources, amid increased competition and cost pressures, has driven the adoption of text Self-Healing Mobile Networks to enhance operational efficiency of current and future wireless networks. This paradigm shift relies on Artificial Intelligence (AI) to increase automation of network functions, notably by applying predictive fault detection and automatic root-cause analysis. In this context, this paper proposes a Deep Learning (DL) model for text self-healing operations based on a Spatial Graph Convolutional Neural Network (SGCN), which is applied to evaluate the performance degradation of Base Stations (BSs) and uncover the underlying root-causes. The advantages of the proposed DL model are threefold. Firstly, it is especially suited for wireless network applications, leveraging the SGCN to account for spatial dependencies among BSs and their physical characteristics. Secondly, the proposed model offers the flexibility to process diverse types of predictive features, including Performance Management (PM), Fault Management (FM), or other data types. Thirdly, it incorporates an explainability module that pinpoints the input features, such as PM counters, with the most significant influence on BS performance, thereby shedding light on its root-cause factors. The proposed model was evaluated on a live 4G network dataset and the results confirmed its effectiveness in identifying BS performance degradation. An F1-score of 89.6% was achieved in the classification of performance failures, which includes a 27% reduction in false negatives compared to prior research outcomes. In a live network environment, this reduction translates into substantial improvements in Quality of Experience (QoE) for the end users and cost savings for the Mobile Network Operators (MNOs).pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationMata L, Sousa M, Vieira P, Queluz MP, Rodrigues A. On the Use of Spatial Graphs for Performance Degradation Root-Cause Analysis Toward Self-Healing Mobile Networks. IEEE Access. 2024; 12, pp. 20490-20508: 3361284. https://doi.org/ 10.1109/ACCESS.2024.3361284pt_PT
dc.identifier.doi10.1109/ACCESS.2024.3361284pt_PT
dc.identifier.issn2169-3536
dc.identifier.urihttp://hdl.handle.net/10400.21/17878
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherIEEEpt_PT
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/10418212pt_PT
dc.subjectartificial intelligencept_PT
dc.subjectdeep learningpt_PT
dc.subjectself-healing operationspt_PT
dc.subjectmobile network performancept_PT
dc.subjectroot-cause analysispt_PT
dc.titleOn the Use of Spatial Graphs for Performance Degradation Root-Cause Analysis Toward Self-Healing Mobile Networkspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage20508pt_PT
oaire.citation.startPage20490pt_PT
oaire.citation.titleIEEE Accesspt_PT
oaire.citation.volume12pt_PT
person.familyNameMaia Bravo da Mata
person.familyNameSousa
person.familyNameVieira
person.familyNameQueluz
person.familyNameRodrigues
person.givenNameLuís Miguel
person.givenNameMarco
person.givenNamePedro
person.givenNameMaria Paula
person.givenNameAntónio
person.identifier.ciencia-id8B17-8BF2-C9BD
person.identifier.ciencia-idCB11-BB4E-3C79
person.identifier.ciencia-id071B-9A70-15B8
person.identifier.ciencia-idC210-DAC9-D03A
person.identifier.ciencia-idC810-67D6-FD83
person.identifier.orcid0000-0003-4572-9156
person.identifier.orcid0000-0002-2471-170X
person.identifier.orcid0000-0003-0279-8741
person.identifier.orcid0000-0003-0266-4022
person.identifier.orcid0000-0003-2115-7245
person.identifier.ridB-5234-2016
person.identifier.scopus-author-id57202674941
person.identifier.scopus-author-id7004567421
person.identifier.scopus-author-id6602528040
person.identifier.scopus-author-id35495905500
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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relation.isAuthorOfPublication.latestForDiscovery26edbc1d-13c3-4495-9bcc-084c089d2325

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