Bivariate Empirical Modeling of Global System for Mobile-Communication (GSM) Propagation Path Loss in an Undulating Terrain

Authors

  • O. Adukwu Department of Industrial and Production Engineering, Federal University of Technology Akure, Akure, Nigeria.
  • E. D. Oku Department of Physics, University of Calabar, Calabar, Nigeria
  • O. I. Apeh Department of Geoinformatics and Surveying, University of Nigeria Enugu Campus, Nigeria

Keywords:

Empirical modeling, propagation path loss, regression model

Abstract

Empirical modeling more accurately represents systems input/output relationship than the more difficult deterministic method. In this work, GSM propagation path loss variation with terrain was modeled using second order regression. Netmonitor on Nokia 3310 measured the path loss at locations separated by 200m up to 4010m. The power signal was obtained from a base transceiver system (BTS) codenamed LBR19 belonging to Globacom Telecommunications Limited. LBR19 is in Ofuegbe, Edo State, Nigeria. Entrex global positioning system (GPS) measured the earth coordinates at these locations. Height above sea level and distance from BTS were used as input variables to the second order regression model obtained from MATLAB statistic toolbox. The obtained model produced a root mean square error of 6.34, P-value of 0.000163 and R-square value of 0.78 which indicates the signiï¬cance of all the terms in the equation and the predictive performance of the obtained model. This approach makes it more flexible to model signal propagation speciï¬c to a particular location accurately and quickly for ease of decision making.

References

Abhayawardhana, V.S., Wassell, I.J., Crosby, D., Sellars, M.P., and Brown, M.G., Comparison of Empirical Propagation Path Loss Models for Fixed Wireless Access Systems, 61s t IEEE Vehicular Technology Conference, Stockholm, Sweden, 2005.

Billings. S. A, Nonlinear System Identification. John Wiley and Sons Limited, West Sussex, United Kingdom. 2013

Braun M.R, Altan, H., and Beck, S.B.N., Using regression analysis to predict the future energy consumption of a supermarket in the UK, Appl. Energy. Vol. 130, pp. 305–313. 2014.

Chai, W. and Qiao J., Non-linear system identification and fault detection method using RBF neural networks with set membership estimation, International Journal of Modeling, Identification and Control, Vol. 20, pp 114-120, 2013.

COST Action-231, Digital Mobile Radio Towards Future generation Systems, final technical report, European Communities, EUR 18957. 1999.

Danladi, A., Wavelet Based Path Loss Modelling for Global System for Mobile Communication in an Urban Environment, International Journal of Science and Research, Vol. 3, pp. 1929-1932, 2014.

Hata, M. Emperical Formula For Propagation Path Loss in Land Mobile Services, IEEE Transactions on Vehicular Technology, Vol. 29, pp. 317-325, 1981.

Okumura, H., Field strength and its Variability in Very high Frequency (VHF) and Ultra High Frequency (UHF) Land-Mobile Radio Services, Review of the Electrical Communication Laboratory, Vol. 16. 1968.

Sharma, P.P. and Singh, R.K., Comparative Analysis of Propagation Path loss Models with Field Measured Data, International Journal of Engineering, Science and Technology, Vol. 2 pp. 2008–2013. 2013.

Sun, S., Rappaport, S.T., Thomas, T.A., Ghosh, A., Nguyen, H. C. Investigation of Prediction Accuracy , Sensitivity , and Parameter Stability of Large-Scale Propagation Path Loss Models for 5G Wireless Communications, IEEE transactions on Vehicular Technology, Vol. 65, pp 2843-2860, 2016.

Xiong, J., Zhang, G., Hu, J. and Wu, L. B. Bead geometry prediction for robotic GMAW-Based Rapid Manufacturing Through a Neural Network and a Second-Order Regression Analysis, Journal of Intelligent Manufacturing, Vol 65, pp.2843-2860, 2014.

Downloads

Published

2019-11-16