Generative Adversarial Networks applied to Telecom Data - Using GANs to generate synthetic features regarding Wi-Fi signal quality

Detalhes bibliográficos
Autor(a) principal: Espindola, Tatiane Sander
Data de Publicação: 2021
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/119708
Resumo: Project Work presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics
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spelling Generative Adversarial Networks applied to Telecom Data - Using GANs to generate synthetic features regarding Wi-Fi signal qualityGenerative ModelsGenerative Adversarial NetworksNeural NetworksMachine LearningSynthetic DataTelecommunicationsProject Work presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsWireless networks are, currently, one of the main technologies used to connect people. Considering the constant advancements in the field, the telecom operators must guarantee a high-quality service to keep their customer portfolio. To ensure this high-quality service, it is common the establishment of partnerships with specialized technology companies that deliver software services to monitor the networks and identify faults and respective solutions. Although, a common barrier faced for these specialized companies is the lack of data to develop and test their products. This project’s purpose was to better understand Generative Adversarial Networks (GANs), an algorithm considered state-of-theart between the generative models, and test its usage to generate synthetic telecommunication data that can fill this gap. To do that, it was developed, trained and compared two of the most used GAN’s architectures, the Vanilla GAN and the WGAN. Both the models presented good results and was able to simulate datasets very similar to the real ones. The WGAN was chosen as the final model, but just for presenting a slightly and subjective better result on the descriptive analysis. In fact, the two models had very similar outputs and both can be used.Castelli, MauroRUNEspindola, Tatiane Sander2021-05-282024-05-28T00:00:00Z2021-05-28T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/119708TID:202734510enginfo:eu-repo/semantics/embargoedAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:02:20Zoai:run.unl.pt:10362/119708Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:44:10.400467Repositório 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 Generative Adversarial Networks applied to Telecom Data - Using GANs to generate synthetic features regarding Wi-Fi signal quality
title Generative Adversarial Networks applied to Telecom Data - Using GANs to generate synthetic features regarding Wi-Fi signal quality
spellingShingle Generative Adversarial Networks applied to Telecom Data - Using GANs to generate synthetic features regarding Wi-Fi signal quality
Espindola, Tatiane Sander
Generative Models
Generative Adversarial Networks
Neural Networks
Machine Learning
Synthetic Data
Telecommunications
title_short Generative Adversarial Networks applied to Telecom Data - Using GANs to generate synthetic features regarding Wi-Fi signal quality
title_full Generative Adversarial Networks applied to Telecom Data - Using GANs to generate synthetic features regarding Wi-Fi signal quality
title_fullStr Generative Adversarial Networks applied to Telecom Data - Using GANs to generate synthetic features regarding Wi-Fi signal quality
title_full_unstemmed Generative Adversarial Networks applied to Telecom Data - Using GANs to generate synthetic features regarding Wi-Fi signal quality
title_sort Generative Adversarial Networks applied to Telecom Data - Using GANs to generate synthetic features regarding Wi-Fi signal quality
author Espindola, Tatiane Sander
author_facet Espindola, Tatiane Sander
author_role author
dc.contributor.none.fl_str_mv Castelli, Mauro
RUN
dc.contributor.author.fl_str_mv Espindola, Tatiane Sander
dc.subject.por.fl_str_mv Generative Models
Generative Adversarial Networks
Neural Networks
Machine Learning
Synthetic Data
Telecommunications
topic Generative Models
Generative Adversarial Networks
Neural Networks
Machine Learning
Synthetic Data
Telecommunications
description Project Work presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics
publishDate 2021
dc.date.none.fl_str_mv 2021-05-28
2021-05-28T00:00:00Z
2024-05-28T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/119708
TID:202734510
url http://hdl.handle.net/10362/119708
identifier_str_mv TID:202734510
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/embargoedAccess
eu_rights_str_mv embargoedAccess
dc.format.none.fl_str_mv 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
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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
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