Path-Loss Prediction for UHF/VHF Signal Propagation in Edo State: Neural Network Approach

Authors

  • Ogbeide K. O. University of Benin
  • Eko Mwenrenren E. J University of Benin

DOI:

https://doi.org/10.34306/csit.v1i2.52

Keywords:

ANN, Hata-model, Pathloss, back-propagation, neurons

Abstract

The aim of this paper is to present and evaluate artificial neural network model used for path loss prediction of signal propagation in the VHF/UHF spectrum in Edo state.Measurement data obtained from three television broadcasting stations in Edo state, operating at 189.25MHz, 479.25MHz, and 743.25MHz, is used to train and evaluate the artificial neural network. A two layer neural network with one hidden and one output layer is evaluated regarding prediction accuracy and generalization properties. The path loss prediction results obtained by using the artificial neural network model are evaluated against the Hata and Walfisch-Ikegami empirical path loss models .Result analysis shows that the artificial neural network performs well as regards to prediction accuracy and generalization ability. The ANN performed better across all performance measures in comparison to the Hata and Walfisch-Ikegami and Line of Sight models in estimating path loss in vhf/uhf spectrum in Edo state.

Downloads

Download data is not yet available.

References

[1] H. Bertoni, “Coverage prediction for mobile radio systems operating in the 800/900 MHz frequency range,”
IEEE Transactions on Vehicular Technology, vol. 37, no. 1, pp. 3–72, February 1988.
[2] C. A. Zelley and C. C. Constantinou, “A three-dimensional parabolic equation applied to VHF/UHF propagation
over irregular terrain,” IEEE Transactions on Antennas and Propagation, vol. 47, no. 10, pp. 1586–1596,
October 1999.
[3] E. Ostlin, et al “In press:Macrocell radio wave propagation prediction using an artificial neural network,” IEEE
Transactions on Vehicular Technology, 2010
[4] K. E. Stocker and F. M. Landstorfer, “Empirical prediction of radiowave propagation by neural network
simulator,” Electronics Letters, vol. 28, no. 12, pp. 1177–1178, June 1992
[5] I. Popescu, et al, “ANN prediction models for outdoor environment,” in 17th IEEE International Symposium on
Personal, Indoor and Mobile Radio Communications, Helsinki, Finland, pp. 1–5, September 2006.
[6] Z. Stankovi et al , “The hybrid-neural empirical model for the electromagnetic field level prediction in urban
environments,” in 7th Seminar on Neural Network Applications in Electrical Engineering, Belgrade, Serbia,
pp. 189–192, September 2004.
[7] A. Ne?skovi´c, N. Ne?skovi´c, and D. Paunovi´c, “Indoor electric field level prediction model based on the
artificial neural networks,” IEEE Communications Letters, vol. 4, no. 6, pp. 190–192, June 2000.
[8] Walter Debus, “RF Path Loss & Transmission Distance Calculations,” Axonn, LLC, Technical Memorandum,
August 4, 2006.
[9] M.H. Beale, Martin T. Hagan, Howard B. Demuth, “Neural Network Toolbox™ User’s Guide R2013a,”
MathWorks, Inc., 2013.
[10] H.Simon, “Neural networks: A comprehensive foundation,” Prentice Hall International, Inc., Second
Edition, 1999.
[11] Ning Qian,”Contributed Article On the momentum term in gradient descent learning algorithms,” Center for
Neurobiology and Behavior, Columbia University, New York, USA Neural Networks vol. 12,
pp. 145–151, 1999.

Downloads

Published

2016-06-01

How to Cite

O., O. K., & E. J, E. M. (2016). Path-Loss Prediction for UHF/VHF Signal Propagation in Edo State: Neural Network Approach. APTIKOM Journal on Computer Science and Information Technologies, 1(2), 77–84. https://doi.org/10.34306/csit.v1i2.52

Issue

Section

Articles