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
Autor(a) principal: | |
---|---|
Data de Publicação: | 2017 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Journal of Microwaves. Optoelectronics and Electromagnetic Applications |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2179-10742017000300708 |
Resumo: | Abstract 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. |
id |
SBMO-1_2000c20c5380197802b0f5463fb86de7 |
---|---|
oai_identifier_str |
oai:scielo:S2179-10742017000300708 |
network_acronym_str |
SBMO-1 |
network_name_str |
Journal of Microwaves. Optoelectronics and Electromagnetic Applications |
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 lossAbstract 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.Sociedade Brasileira de Microondas e Optoeletrônica e Sociedade Brasileira de Eletromagnetismo2017-09-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S2179-10742017000300708Journal of Microwaves, Optoelectronics and Electromagnetic Applications v.16 n.3 2017reponame:Journal of Microwaves. Optoelectronics and Electromagnetic Applicationsinstname:Sociedade Brasileira de Microondas e Optoeletrônica (SBMO)instacron:SBMO10.1590/2179-10742017v16i3925info:eu-repo/semantics/openAccessCavalcanti,Bruno J.Cavalcante,Gustavo A.Mendonça,Laércio M. deCantanhede,Gabriel M.Oliveira,Marcelo M.M. deD’Assunção,Adaildo G.eng2017-10-06T00:00:00Zoai:scielo:S2179-10742017000300708Revistahttp://www.jmoe.org/index.php/jmoe/indexONGhttps://old.scielo.br/oai/scielo-oai.php||editor_jmoe@sbmo.org.br2179-10742179-1074opendoar:2017-10-06T00:00Journal of Microwaves. Optoelectronics and Electromagnetic Applications - Sociedade Brasileira de Microondas e Optoeletrônica (SBMO)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 Cavalcanti,Bruno J. 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 |
author |
Cavalcanti,Bruno J. |
author_facet |
Cavalcanti,Bruno J. Cavalcante,Gustavo A. Mendonça,Laércio M. de Cantanhede,Gabriel M. Oliveira,Marcelo M.M. de D’Assunção,Adaildo G. |
author_role |
author |
author2 |
Cavalcante,Gustavo A. Mendonça,Laércio M. de Cantanhede,Gabriel M. Oliveira,Marcelo M.M. de D’Assunção,Adaildo G. |
author2_role |
author author author author author |
dc.contributor.author.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. |
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 |
Abstract 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 |
2017-09-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2179-10742017000300708 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2179-10742017000300708 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/2179-10742017v16i3925 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Sociedade Brasileira de Microondas e Optoeletrônica e Sociedade Brasileira de Eletromagnetismo |
publisher.none.fl_str_mv |
Sociedade Brasileira de Microondas e Optoeletrônica e Sociedade Brasileira de Eletromagnetismo |
dc.source.none.fl_str_mv |
Journal of Microwaves, Optoelectronics and Electromagnetic Applications v.16 n.3 2017 reponame:Journal of Microwaves. Optoelectronics and Electromagnetic Applications instname:Sociedade Brasileira de Microondas e Optoeletrônica (SBMO) instacron:SBMO |
instname_str |
Sociedade Brasileira de Microondas e Optoeletrônica (SBMO) |
instacron_str |
SBMO |
institution |
SBMO |
reponame_str |
Journal of Microwaves. Optoelectronics and Electromagnetic Applications |
collection |
Journal of Microwaves. Optoelectronics and Electromagnetic Applications |
repository.name.fl_str_mv |
Journal of Microwaves. Optoelectronics and Electromagnetic Applications - Sociedade Brasileira de Microondas e Optoeletrônica (SBMO) |
repository.mail.fl_str_mv |
||editor_jmoe@sbmo.org.br |
_version_ |
1752122126218821632 |