1. Universidade Federal do Rio Grande do Norte
2. Centro de Tecnologia
3. CT - DCO - Departamento de Engenharia de Comunicações
4. CT - DCO - Artigos publicados em periódicos

Título:	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(es):	D´Assunção, Adaildo Gomes Cavalcanti, Bruno J. Cavalcante, Gustavo A. Mendonça, Laércio M. de Cantanhede, Gabriel Moura Oliveira, Marcelo M.M.de
Palavras-chave:	Artificial Neural Networks – ANN;Long Term Evolution – LTE;Long Term Evolution Advanced – LTE-A;Propagation models;Path loss
Data do documento:	Set-2017
Editor:	Scielo
Referência:	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.
Resumo:	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
URI:	https://repositorio.ufrn.br/handle/123456789/31577
ISSN:	2179-1074
Aparece nas coleções:	CT - DCO - Artigos publicados em periódicos

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