Using Deep-Learning for 5G End-to-End Delay Estimation Based on Gaussian Mixture Models
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10362/163994 |
Resumo: | Publisher Copyright: © 2023 by the authors. |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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7160 |
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Using Deep-Learning for 5G End-to-End Delay Estimation Based on Gaussian Mixture Modelsend-to-end delayestimationheterogeneous networksmachine learningquality of serviceInformation SystemsPublisher Copyright: © 2023 by the authors.Deep 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.DEE - Departamento de Engenharia Electrotécnica e de ComputadoresRUNFadhil, DiyarOliveira, Rodolfo2024-02-22T23:54:18Z2023-12-052023-12-05T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article12application/pdfhttp://hdl.handle.net/10362/163994eng2078-2489PURE: 83898202https://doi.org/10.3390/info14120648info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:50:10Zoai:run.unl.pt:10362/163994Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:59:59.124431Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Using Deep-Learning for 5G End-to-End Delay Estimation Based on Gaussian Mixture Models |
title |
Using Deep-Learning for 5G End-to-End Delay Estimation Based on Gaussian Mixture Models |
spellingShingle |
Using Deep-Learning for 5G End-to-End Delay Estimation Based on Gaussian Mixture Models Fadhil, Diyar end-to-end delay estimation heterogeneous networks machine learning quality of service Information Systems |
title_short |
Using Deep-Learning for 5G End-to-End Delay Estimation Based on Gaussian Mixture Models |
title_full |
Using Deep-Learning for 5G End-to-End Delay Estimation Based on Gaussian Mixture Models |
title_fullStr |
Using Deep-Learning for 5G End-to-End Delay Estimation Based on Gaussian Mixture Models |
title_full_unstemmed |
Using Deep-Learning for 5G End-to-End Delay Estimation Based on Gaussian Mixture Models |
title_sort |
Using Deep-Learning for 5G End-to-End Delay Estimation Based on Gaussian Mixture Models |
author |
Fadhil, Diyar |
author_facet |
Fadhil, Diyar Oliveira, Rodolfo |
author_role |
author |
author2 |
Oliveira, Rodolfo |
author2_role |
author |
dc.contributor.none.fl_str_mv |
DEE - Departamento de Engenharia Electrotécnica e de Computadores RUN |
dc.contributor.author.fl_str_mv |
Fadhil, Diyar Oliveira, Rodolfo |
dc.subject.por.fl_str_mv |
end-to-end delay estimation heterogeneous networks machine learning quality of service Information Systems |
topic |
end-to-end delay estimation heterogeneous networks machine learning quality of service Information Systems |
description |
Publisher Copyright: © 2023 by the authors. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-12-05 2023-12-05T00:00:00Z 2024-02-22T23:54:18Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10362/163994 |
url |
http://hdl.handle.net/10362/163994 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2078-2489 PURE: 83898202 https://doi.org/10.3390/info14120648 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
12 application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799138176389873664 |