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

Bruno Jácome Cavalcanti, Gustavo A. Cavalcante, Laércio M Mendonça, Gabriel Cantanhede, Marcelo M.M. Oliveira, Adaildo G. D'Assunção


This article presents the development and analysis of a hybrid, error correction-based, neural network 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 backpropagation Artificial Neural Network (ANN). The network performance was tested along with two optimization techniques - Genetic Algorithm (GA) and Least Mean Square (LMS). 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 presented the best results, indicating greater similarity with experimental data. The results developed in this research will help to achieve better signal estimation, reducing errors in planning and implementation of LTE and LTE-A systems.


Artificial Neural Networks ANN, Genetic Algorithm GA; Least Mean Square LMS, ; Long Term Evolution LTE; Long Term Evolution Advanced LTE-A; propagation models

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DOI: http://dx.doi.org/10.1590/2179-10742017v16i3925


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