The Prediction of Propagation Loss of FM Radio Station Using Artificial Neural Network

Ali Riza Ozdemir¹, Mustafa Alkan², Mehmet Kabak³, Mehmet Gulsen⁴, Murat Hüsnü Sazli^5*

¹Information and Communications Technologies Authority, Spectrum Monitoring Department, Ankara, Turkey.
²Department of Electrical and Electronics Engineering, Faculty of Technology, Universityof Gazi, Ankara, Turkey.
³Physics Engineering Department, Faculty of Science, University of Ankara, Ankara, Turkey.
⁴Information and Communications Technologies Authority, Ankara, Turkey.
⁵Electrical and Electronics Engineering Department, Faculty of Engineering, University of Ankara, Ankara, Turkey.

DOI: 10.4236/jemaa.2014.611036                

Abstract

In order to calculate the propagation loss of electromagnetic waves produced by a transmitter, a variety of models based on empirical and deterministic formulas are used. In this study, one of the artificial neural networks models, Levenberg-Marquardt algorithm, which is quite effective for predicting the propagation is used and the results obtained by this algorithm are compared with the simulation results based on ITU-R 1546 and Epstein-Peterson models. In this paper, the propagation loss of FM radio station using artificial neural networks models is studied depending on the Levenberg-Marquardt algorithm. For training the artificial neural network, as the input data; range (r), effective antenna height (h) and terrain irregularity (△H) parameters are involved and measured values are treated as the output data. The good results obtained in the city area reveal that the artificial neural network is a very efficient method to compute models which integrate theoretical and experimental data. Meanwhile, the results show that an ANN model performs very well compared with theoretical and empiric propagation models with regard to prediction accuracy, complexity, and prediction time. By comparing the results, the RMSE for Neural Network Model using Levenberg-Marquardt is 9.57, and it is lower than that of classical propagation model using Epstein-Peterson for which RMSE is 10.26.

Keywords

Artificial Neural Network, Prediction of Propagation

Conflicts of Interest

The authors declare no conflicts of interest.

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