TITLE:
The Prediction of Propagation Loss of FM Radio Station Using Artificial Neural Network
AUTHORS:
Ali Riza Ozdemir, Mustafa Alkan, Mehmet Kabak, Mehmet Gulsen, Murat Hüsnü Sazli
KEYWORDS:
Artificial Neural Network, Prediction of Propagation
JOURNAL NAME:
Journal of Electromagnetic Analysis and Applications,
Vol.6 No.11,
September
29,
2014
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.