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Using Deep-Learning for 5G End-to-End Delay Estimation Based on Gaussian Mixture Models

2023-12-05Artigo científico Acesso aberto
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dc.contributor.authorFadhil, Diyar
dc.contributor.authorOliveira, Rodolfo
dc.contributor.institutionDEE - Departamento de Engenharia Electrotécnica e de Computadores
dc.contributor.pblMDPI - Multidisciplinary Digital Publishing Institute
dc.date.accessioned2024-02-22T23:54:18Z
dc.date.available2024-02-22T23:54:18Z
dc.date.issued2023-12-05
dc.descriptionPublisher Copyright: © 2023 by the authors.
dc.description.abstractDeep learning is used in various applications due to its advantages over traditional Machine Learning (ML) approaches in tasks encompassing complex pattern learning, automatic feature extraction, scalability, adaptability, and performance in general. This paper proposes an end-to-end (E2E) delay estimation method for 5G networks through deep learning (DL) techniques based on Gaussian Mixture Models (GMM). In the first step, the components of a GMM are estimated through the Expectation-Maximization (EM) algorithm and are subsequently used as labeled data in a supervised deep learning stage. A multi-layer neural network model is trained using the labeled data and assuming different numbers of E2E delay observations for each training sample. The accuracy and computation time of the proposed deep learning estimator based on the Gaussian Mixture Model (DLEGMM) are evaluated for different 5G network scenarios. The simulation results show that the DLEGMM outperforms the GMM method based on the EM algorithm, in terms of the accuracy of the E2E delay estimates, although requiring a higher computation time. The estimation method is characterized for different 5G scenarios, and when compared to GMM, DLEGMM reduces the mean squared error (MSE) obtained with GMM between 1.7 to 2.6 times.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent12
dc.format.extent518889
dc.identifier.doi10.3390/info14120648
dc.identifier.issn2078-2489
dc.identifier.otherPURE: 83898202
dc.identifier.otherPURE UUID: 042350e0-8373-4cc8-9379-ae7ee2d28a76
dc.identifier.otherScopus: 85180370024
dc.identifier.otherWOS: 001131461900001
dc.identifier.urihttp://hdl.handle.net/10362/163994
dc.identifier.urlhttps://www.scopus.com/pages/publications/85180370024
dc.language.isoeng
dc.peerreviewedyes
dc.relationFunding Information: info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50008%2F2020/PT
dc.subjectend-to-end delay
dc.subjectestimation
dc.subjectheterogeneous networks
dc.subjectmachine learning
dc.subjectquality of service
dc.subjectInformation Systems
dc.titleUsing Deep-Learning for 5G End-to-End Delay Estimation Based on Gaussian Mixture Modelsen
dc.typejournal article
degois.publication.issue12
degois.publication.titleInformation (Switzerland)
degois.publication.volume14
dspace.entity.typePublication
rcaap.rightsopenAccess

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