Comparison of Artificial Intelligence and Semi-Empirical Methodologies for Estimation of Coverage in Mobile Networks

Detalhes bibliográficos
Autor(a) principal: Fernandes, Daniel F. S.
Data de Publicação: 2020
Outros Autores: Raimundo, António, Cercas, Francisco, Sebastião, Pedro J. A., Dinis, Rui, Ferreira, Lucio S.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/120922
Resumo: Project 023304 UIDB/04111/2020
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spelling Comparison of Artificial Intelligence and Semi-Empirical Methodologies for Estimation of Coverage in Mobile Networksartificial intelligenceCoverage estimationdrive testsmeasurementsnetwork planningpropagation modelComputer Science(all)Materials Science(all)Engineering(all)Project 023304 UIDB/04111/2020To help telecommunication operators in their network planning, namely coverage estimation and optimisation tasks, this article presents a comparison between a semi-empirical propagation model and a propagation model generated using Artificial Intelligence (AI). These two types of propagation models are quite different in their design. The semi-empiric Automatically Calibrated Standard Propagation Model (ACSPM) is specific for an operating antenna, being calibrated every time a use case application is used and the Artificial Intelligence Propagation Model (AIPM) can be applied in different scenarios, once trained, allowing to estimate coverage for a new antenna location, using information from neighboring antennas. These models have quite different features and applicability. The ACSPM should be applied in network optimisation, when using data from the current state of the antennas. The AIPM can be used in the deployment of new antennas, as it uses data from a certain geographical area. For a better comparison of the models studied, extensive Drive Tests (DT) collection campaigns conducted by operators are used, since coverage estimations are more realistic when DTs are considered. Both models are generated using very different methodologies, but their resulting performance is very similar. The AIPM achieves a Mean Absolute Error (MAE) up to 6.1 dB with a standard deviation of 4 dB. When compared to the ACSPM we have an improvement of 0.5 dB, since this only achieves a MAE up to 6.6 dB. AIPM achieves better results and is the characterised for being completely agnostic and definition-free, when compared with known propagation models.DEE - Departamento de Engenharia Electrotécnica e de ComputadoresDEE2010-A1 TelecomunicaçõesRUNFernandes, Daniel F. S.Raimundo, AntónioCercas, FranciscoSebastião, Pedro J. A.Dinis, RuiFerreira, Lucio S.2021-07-12T22:18:10Z20202020-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article10application/pdfhttp://hdl.handle.net/10362/120922engPURE: 32502791https://doi.org/10.1109/ACCESS.2020.3013036info: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:RCAAP2024-03-11T05:03:18Zoai:run.unl.pt:10362/120922Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:44:28.247561Repositório 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 Comparison of Artificial Intelligence and Semi-Empirical Methodologies for Estimation of Coverage in Mobile Networks
title Comparison of Artificial Intelligence and Semi-Empirical Methodologies for Estimation of Coverage in Mobile Networks
spellingShingle Comparison of Artificial Intelligence and Semi-Empirical Methodologies for Estimation of Coverage in Mobile Networks
Fernandes, Daniel F. S.
artificial intelligence
Coverage estimation
drive tests
measurements
network planning
propagation model
Computer Science(all)
Materials Science(all)
Engineering(all)
title_short Comparison of Artificial Intelligence and Semi-Empirical Methodologies for Estimation of Coverage in Mobile Networks
title_full Comparison of Artificial Intelligence and Semi-Empirical Methodologies for Estimation of Coverage in Mobile Networks
title_fullStr Comparison of Artificial Intelligence and Semi-Empirical Methodologies for Estimation of Coverage in Mobile Networks
title_full_unstemmed Comparison of Artificial Intelligence and Semi-Empirical Methodologies for Estimation of Coverage in Mobile Networks
title_sort Comparison of Artificial Intelligence and Semi-Empirical Methodologies for Estimation of Coverage in Mobile Networks
author Fernandes, Daniel F. S.
author_facet Fernandes, Daniel F. S.
Raimundo, António
Cercas, Francisco
Sebastião, Pedro J. A.
Dinis, Rui
Ferreira, Lucio S.
author_role author
author2 Raimundo, António
Cercas, Francisco
Sebastião, Pedro J. A.
Dinis, Rui
Ferreira, Lucio S.
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv DEE - Departamento de Engenharia Electrotécnica e de Computadores
DEE2010-A1 Telecomunicações
RUN
dc.contributor.author.fl_str_mv Fernandes, Daniel F. S.
Raimundo, António
Cercas, Francisco
Sebastião, Pedro J. A.
Dinis, Rui
Ferreira, Lucio S.
dc.subject.por.fl_str_mv artificial intelligence
Coverage estimation
drive tests
measurements
network planning
propagation model
Computer Science(all)
Materials Science(all)
Engineering(all)
topic artificial intelligence
Coverage estimation
drive tests
measurements
network planning
propagation model
Computer Science(all)
Materials Science(all)
Engineering(all)
description Project 023304 UIDB/04111/2020
publishDate 2020
dc.date.none.fl_str_mv 2020
2020-01-01T00:00:00Z
2021-07-12T22:18:10Z
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/10362/120922
url http://hdl.handle.net/10362/120922
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv PURE: 32502791
https://doi.org/10.1109/ACCESS.2020.3013036
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 10
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
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