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

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DOI: 10.4236/jemaa.2014.611036    2,638 Downloads   3,273 Views   Citations

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.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Ozdemir, A. , Alkan, M. , Kabak, M. , Gulsen, M. and Sazli, M. (2014) The Prediction of Propagation Loss of FM Radio Station Using Artificial Neural Network. Journal of Electromagnetic Analysis and Applications, 6, 358-365. doi: 10.4236/jemaa.2014.611036.

References

[1] (2007) ITU-R Recommendation P. 1546-3. Method for Point-to-Area Predictions for Terrestrial Services in the Frequency Range 30 MHz to 3000 MHz.
[2] Epstein, J. and Peterson, D.W. (1953) An Experimental Study of Wave Propagation at 850 Mc/s. Proceedings of the Institute of Radio Engineers, 41, 595-611.
[3] Ostlin, E. (2010) Macrocell Path-Loss Prediction Using Artificial Neural Networks. IEEE Transactions on Vehicular Technology, 59, 2735-2746.
http://dx.doi.org/10.1109/TVT.2010.2050502
[4] Haykin, S. (1994) Neural Networks: A Comprehensive Foundation. McMillan College Publishing Co., New York.
[5] Rojas, R. (1996) Neural Networks a Systematic Introduction. Springer-Verlag, Berlin.
[6] Horn, R.A. and Johnson, C.R. (1985) Matris Analysis. Cambridge University Press, New York.
http://dx.doi.org/10.1017/CBO9780511810817
[7] Popescu, I., Kanstas, A., Angelou, E., Nafornita, L. and Constantinou, P. (2002) Applications of Generalized RBF-NN for Path Loss Prediction. The 13th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, Lisbon, 15-18 September 2002, 484-488.
[8] Piacentini, M. and Rinaldi, F. (2010) Path Loss Prediction in Urban Environment Using Learning Machines and Dimensionality Reduction Techniques. Computational Management Science, 8, 371-385.
[9] Ostlin, E. (2012) IEEE Macrocell Path-Loss Prediction Using Artificial Neural Networks. Transactions on Vehicular Technology, 59, 2735-2746.
[10] Balandier, T., Camanida, A., Lemonie, V. and Alexandre, F. (1995) 170 MHz Field Strength Prediction in Urban Environments Using Neural Nets. IEEE 6th International Symposium on Personnel, Indoor and Mobile Radio Communications (PIMRC), Toronto, 27-29 September 1995.
[11] Wolfle, G. and Landstorfer, F.M. (1997) Field Strength Prediction with Dominant Paths and Neural Network, in MIOP 1997. Sindenfilgen, Germany, 216-220.
[12] Leros, A.P., Alexandridis, A.A., Dangakis, K. and Kostarakis, P. (1998) Evaluation of Radio Propagation Parameters for Field Strength Prediction Using Neural Networks. IEEE Conference on Antennas and Propagation for Wireless Communications, Waltham, 1-4 November 1998, 17-20.

  
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