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A machine learning driven methodology for alarm prediction towards self-healing in wireless networks

2024-05-24Conference object 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.accessioned2024-11-06T08:43:50Z
dc.date.available2024-11-06T08:43:50Z
dc.date.issued2024-05-24
dc.description.abstractAlthough Artificial Intelligence (AI) is already used by 5th Generation (5G) to support specific network functions, the increased complexity of 6th Generation (6G) will demand the adoption of extended AI capabilities to enhance network efficiency. Moreover, high network performance and availability at a sustainable cost will be crucial to emerging applications, such as autonomous vehicles and smart cities. In this context, operators are expected to implement Self-Healing Operations (SHOs) to transition from reactive handling of network faults to a preventive approach, relying on statistical learning of network data. This paper proposes a Machine Learning (ML)-driven methodology to predict network faults using generic Fault Management (FM) data, enabling the implementation of preventive actions to avoid service degradation or failure. The evaluation of this methodology using live network data revealed statistical associations among certain network faults, considering both time and root-cause factors. Therefore, FM data and two ML models, namely Logistic Regression (LR) and Light Gradient Boosting Model (LGBM), were used to predict network faults, achieving a 93% success rate within a 60-minute anticipation period.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationL. Mata, M. Sousa, P. Vieira, M. P. Queluz and A. Rodrigues, "A Machine Learning Driven Methodology for Alarm Prediction Towards Self-Healing in Wireless Networks," 2024 Wireless Telecommunications Symposium (WTS), Oakland, CA, USA, 2024, pp. 1-6, doi: 10.1109/WTS60164.2024.10536674pt_PT
dc.identifier.doi10.1109/WTS60164.2024.10536674pt_PT
dc.identifier.eissn2690-8336
dc.identifier.isbn979-8-3503-1789-3
dc.identifier.issn979-8-3503-1788-6
dc.identifier.issn1934-5070
dc.identifier.urihttp://hdl.handle.net/10400.21/17837
dc.language.isoengpt_PT
dc.publisherIEEEpt_PT
dc.relationInstituto de Telecomunicações
dc.relation.publisherversionhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10536674pt_PT
dc.subjectmobile networkspt_PT
dc.subjectself-healing operationspt_PT
dc.subjectpredictive fault managementpt_PT
dc.subjectmachine learningpt_PT
dc.titleA machine learning driven methodology for alarm prediction towards self-healing in wireless networkspt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.awardTitleInstituto de Telecomunicações
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50008%2F2020/PT
oaire.citation.conferencePlace10-12 April 2024 - Oakland, CA, USApt_PT
oaire.citation.endPage6pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.title2024 Wireless Telecommunications Symposium (WTS)pt_PT
oaire.fundingStream6817 - DCRRNI ID
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
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT
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