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...

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Principais autores: D´Assunção, Adaildo Gomes, Cavalcanti, Bruno J., Cavalcante, Gustavo A., Mendonça, Laércio M. de, Cantanhede, Gabriel Moura, Oliveira, Marcelo M.M.de
Formato: article
Idioma:English
Publicado em: Scielo
Assuntos:
Artificial Neural Networks – ANN
Long Term Evolution – LTE
Long Term Evolution Advanced – LTE-A
Propagation models
Path loss
Endereço do item:https://repositorio.ufrn.br/handle/123456789/31577
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spelling ri-123456789-315772021-02-21T08:31:52Z 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 Cavalcanti, Bruno J. Cavalcante, Gustavo A. Mendonça, Laércio M. de Cantanhede, Gabriel Moura Oliveira, Marcelo M.M.de Artificial Neural Networks – ANN Long Term Evolution – LTE Long Term Evolution Advanced – LTE-A Propagation models Path loss 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 2021-02-19T20:19:58Z 2021-02-19T20:19:58Z 2017-09 article 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 en Attribution-NonCommercial 3.0 Brazil http://creativecommons.org/licenses/by-nc/3.0/br/ application/pdf Scielo
institution Repositório Institucional
collection RI - UFRN
language English
topic Artificial Neural Networks – ANN
Long Term Evolution – LTE
Long Term Evolution Advanced – LTE-A
Propagation models
Path loss
spellingShingle Artificial Neural Networks – ANN
Long Term Evolution – LTE
Long Term Evolution Advanced – LTE-A
Propagation models
Path loss
D´Assunção, Adaildo Gomes
Cavalcanti, Bruno J.
Cavalcante, Gustavo A.
Mendonça, Laércio M. de
Cantanhede, Gabriel Moura
Oliveira, Marcelo M.M.de
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
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
format article
author D´Assunção, Adaildo Gomes
Cavalcanti, Bruno J.
Cavalcante, Gustavo A.
Mendonça, Laércio M. de
Cantanhede, Gabriel Moura
Oliveira, Marcelo M.M.de
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_sort D´Assunção, Adaildo Gomes
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
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 hybrid path loss prediction model based on artificial neural networks using empirical models for lte and lte-a at 800 mhz and 2600 mhz
publisher Scielo
publishDate 2021
url https://repositorio.ufrn.br/handle/123456789/31577
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