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

Detalhes bibliográficos
Autor(a) principal: Fadhil, Diyar
Data de Publicação: 2023
Outros Autores: Oliveira, Rodolfo
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.
id RCAP_fc462dfa6389968baa0a69f59ceabfce
oai_identifier_str oai:run.unl.pt:10362/163994
network_acronym_str RCAP
network_name_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository_id_str 7160
spelling 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
repository.mail.fl_str_mv
_version_ 1799138176389873664

Registros relacionados