A Hybrid path loss prediction model based on artificial neural networks using empirical models for LTE and LTE-A at 800 MHz and 2600 MHz

This article presents the analysis of a hybrid, error correction-based, neural network model to predict the path loss for suburban areas at 800 MHz and 2600 MHz, obtained by combining empirical propagation models, ECC-33, Ericsson 9999, Okumura Hata, and 3GPP’s TR 36.942, with a feedforward Artifici...

Descripción completa

Detalles Bibliográficos
Autores: D´Assunção, Adaildo Gomes, Cavalcanti, Bruno J., Cavalcante, Gustavo A., Mendonça, Laércio M. de, Cantanhede, Gabriel Moura, Oliveira, Marcelo M.M.de
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2017
País:Brasil
Institución:Universidade Federal do Rio Grande do Norte (UFRN)
Repositorio:Repositório Institucional da UFRN
Idioma:inglés
OAI Identifier:oai:repositorio.ufrn.br:123456789/31577
Acceso en línea:https://repositorio.ufrn.br/handle/123456789/31577
Access Level:acceso abierto
Palabra clave:Artificial Neural Networks – ANN
Long Term Evolution – LTE
Long Term Evolution Advanced – LTE-A
Propagation models
Path loss
id BR_d87cc1601ac25bf9e5de811e1e0925bb
oai_identifier_str oai:repositorio.ufrn.br:123456789/31577
network_acronym_str BR
network_name_str Brasil
repository_id_str
spelling A Hybrid path loss prediction model based on artificial neural networks using empirical models for LTE and LTE-A at 800 MHz and 2600 MHzArtificial Neural Networks – ANNLong Term Evolution – LTELong Term Evolution Advanced – LTE-APropagation modelsPath lossThis article presents the analysis of a hybrid, error correction-based, neural network model to predict the path loss for suburban areas at 800 MHz and 2600 MHz, obtained by combining empirical propagation models, ECC-33, Ericsson 9999, Okumura Hata, and 3GPP’s TR 36.942, with a feedforward Artificial Neural Network (ANN). The performance of the hybrid model was compared against regular versions of the empirical models and a simple neural network fed with input parameters commonly used in related works. Results were compared with data obtained by measurements performed in the vicinity of the Federal University of Rio Grande do Norte (UFRN), in the city of Natal, Brazil. In the end, the hybrid neural network obtained the lowest RMSE indexes, besides almost equalizing the distribution of simulated and experimental data, indicating greater similarity with measurementsScielo2021-02-19T20:19:58Z2021-02-19T20:19:58Z2017-09info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfCAVALCANTI, Bruno J.; CAVALCANTE, Gustavo A.; MENDONÇA, Laércio M. de; CANTANHEDE, Gabriel M.; OLIVEIRA, Marcelo M.M. de; D’ASSUNÇÃO, Adaildo G.. A Hybrid Path Loss Prediction Model based on Artificial Neural Networks using Empirical Models for LTE And LTE-A at 800 MHz and 2600 MHz. Journal of Microwaves, Optoelectronics And Electromagnetic Applications, [S.L.], v. 16, n. 3, p. 708-722, set. 2017. Disponível em: https://www.scielo.br/scielo.php?script=sci_arttext&pid=S2179-10742017000300708&lng=en&tlng=en. Acesso em: 20 out. 2020. http://dx.doi.org/10.1590/2179-10742017v16i3925.2179-1074https://repositorio.ufrn.br/handle/123456789/3157710.1590/2179-10742017v16i3925ark:/41046/00130000236zqAttribution-NonCommercial 3.0 Brazilhttp://creativecommons.org/licenses/by-nc/3.0/br/info:eu-repo/semantics/openAccessengreponame:Repositório Institucional da UFRNinstname:Universidade Federal do Rio Grande do Norte (UFRN)instacron:UFRND´Assunção, Adaildo GomesCavalcanti, Bruno J.Cavalcante, Gustavo A.Mendonça, Laércio M. deCantanhede, Gabriel MouraOliveira, Marcelo M.M.de2021-02-21T08:31:52Zoai:repositorio.ufrn.br:123456789/31577Repositório InstitucionalPUBhttp://repositorio.ufrn.br/oai/repositorio@bczm.ufrn.bropendoar:2021-02-21T08:31:52Repositório Institucional da UFRN - Universidade Federal do Rio Grande do Norte (UFRN)false
dc.title.none.fl_str_mv A Hybrid path loss prediction model based on artificial neural networks using empirical models for LTE and LTE-A at 800 MHz and 2600 MHz
title A Hybrid path loss prediction model based on artificial neural networks using empirical models for LTE and LTE-A at 800 MHz and 2600 MHz
spellingShingle A Hybrid path loss prediction model based on artificial neural networks using empirical models for LTE and LTE-A at 800 MHz and 2600 MHz
D´Assunção, Adaildo Gomes
Artificial Neural Networks – ANN
Long Term Evolution – LTE
Long Term Evolution Advanced – LTE-A
Propagation models
Path loss
title_short A Hybrid path loss prediction model based on artificial neural networks using empirical models for LTE and LTE-A at 800 MHz and 2600 MHz
title_full A Hybrid path loss prediction model based on artificial neural networks using empirical models for LTE and LTE-A at 800 MHz and 2600 MHz
title_fullStr A Hybrid path loss prediction model based on artificial neural networks using empirical models for LTE and LTE-A at 800 MHz and 2600 MHz
title_full_unstemmed A Hybrid path loss prediction model based on artificial neural networks using empirical models for LTE and LTE-A at 800 MHz and 2600 MHz
title_sort A Hybrid path loss prediction model based on artificial neural networks using empirical models for LTE and LTE-A at 800 MHz and 2600 MHz
dc.creator.none.fl_str_mv D´Assunção, Adaildo Gomes
Cavalcanti, Bruno J.
Cavalcante, Gustavo A.
Mendonça, Laércio M. de
Cantanhede, Gabriel Moura
Oliveira, Marcelo M.M.de
author D´Assunção, Adaildo Gomes
author_facet D´Assunção, Adaildo Gomes
Cavalcanti, Bruno J.
Cavalcante, Gustavo A.
Mendonça, Laércio M. de
Cantanhede, Gabriel Moura
Oliveira, Marcelo M.M.de
author_role author
author2 Cavalcanti, Bruno J.
Cavalcante, Gustavo A.
Mendonça, Laércio M. de
Cantanhede, Gabriel Moura
Oliveira, Marcelo M.M.de
author2_role author
author
author
author
author
dc.subject.por.fl_str_mv Artificial Neural Networks – ANN
Long Term Evolution – LTE
Long Term Evolution Advanced – LTE-A
Propagation models
Path loss
topic Artificial Neural Networks – ANN
Long Term Evolution – LTE
Long Term Evolution Advanced – LTE-A
Propagation models
Path loss
description This article presents the analysis of a hybrid, error correction-based, neural network model to predict the path loss for suburban areas at 800 MHz and 2600 MHz, obtained by combining empirical propagation models, ECC-33, Ericsson 9999, Okumura Hata, and 3GPP’s TR 36.942, with a feedforward Artificial Neural Network (ANN). The performance of the hybrid model was compared against regular versions of the empirical models and a simple neural network fed with input parameters commonly used in related works. Results were compared with data obtained by measurements performed in the vicinity of the Federal University of Rio Grande do Norte (UFRN), in the city of Natal, Brazil. In the end, the hybrid neural network obtained the lowest RMSE indexes, besides almost equalizing the distribution of simulated and experimental data, indicating greater similarity with measurements
publishDate 2017
dc.date.none.fl_str_mv 2017-09
2021-02-19T20:19:58Z
2021-02-19T20:19:58Z
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 CAVALCANTI, Bruno J.; CAVALCANTE, Gustavo A.; MENDONÇA, Laércio M. de; CANTANHEDE, Gabriel M.; OLIVEIRA, Marcelo M.M. de; D’ASSUNÇÃO, Adaildo G.. A Hybrid Path Loss Prediction Model based on Artificial Neural Networks using Empirical Models for LTE And LTE-A at 800 MHz and 2600 MHz. Journal of Microwaves, Optoelectronics And Electromagnetic Applications, [S.L.], v. 16, n. 3, p. 708-722, set. 2017. Disponível em: https://www.scielo.br/scielo.php?script=sci_arttext&pid=S2179-10742017000300708&lng=en&tlng=en. Acesso em: 20 out. 2020. http://dx.doi.org/10.1590/2179-10742017v16i3925.
2179-1074
https://repositorio.ufrn.br/handle/123456789/31577
10.1590/2179-10742017v16i3925
dc.identifier.dark.fl_str_mv ark:/41046/00130000236zq
identifier_str_mv CAVALCANTI, Bruno J.; CAVALCANTE, Gustavo A.; MENDONÇA, Laércio M. de; CANTANHEDE, Gabriel M.; OLIVEIRA, Marcelo M.M. de; D’ASSUNÇÃO, Adaildo G.. A Hybrid Path Loss Prediction Model based on Artificial Neural Networks using Empirical Models for LTE And LTE-A at 800 MHz and 2600 MHz. Journal of Microwaves, Optoelectronics And Electromagnetic Applications, [S.L.], v. 16, n. 3, p. 708-722, set. 2017. Disponível em: https://www.scielo.br/scielo.php?script=sci_arttext&pid=S2179-10742017000300708&lng=en&tlng=en. Acesso em: 20 out. 2020. http://dx.doi.org/10.1590/2179-10742017v16i3925.
2179-1074
10.1590/2179-10742017v16i3925
ark:/41046/00130000236zq
url https://repositorio.ufrn.br/handle/123456789/31577
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv Attribution-NonCommercial 3.0 Brazil
http://creativecommons.org/licenses/by-nc/3.0/br/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution-NonCommercial 3.0 Brazil
http://creativecommons.org/licenses/by-nc/3.0/br/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Scielo
publisher.none.fl_str_mv Scielo
dc.source.none.fl_str_mv reponame:Repositório Institucional da UFRN
instname:Universidade Federal do Rio Grande do Norte (UFRN)
instacron:UFRN
instname_str Universidade Federal do Rio Grande do Norte (UFRN)
instacron_str UFRN
institution UFRN
reponame_str Repositório Institucional da UFRN
collection Repositório Institucional da UFRN
repository.name.fl_str_mv Repositório Institucional da UFRN - Universidade Federal do Rio Grande do Norte (UFRN)
repository.mail.fl_str_mv repositorio@bczm.ufrn.br
_version_ 1853668183835148288
score 15,300724

Similares en LA Referencia