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Comparative Analysis of Basic Models and Artificial Neural Network Based Model for Path Loss Prediction

By Julia Ofure Eichie, Onyedi David Oyedum, Moses Ajewole, and Abiodun Musa Aibinu
Progress In Electromagnetics Research M, Vol. 61, 133-146, 2017


Propagation path loss models are useful for the prediction of received signal strength at a given distance from the transmitter; estimation of radio coverage areas of Base Transceiver Stations (BTS); frequency assignments; interference analysis; handover optimisation; and power level adjustments. Due to the differences in: environmental structures; local terrain profiles; and weather conditions, path loss prediction model for a given environment using any of the existing basic empirical models such as the Okumura-Hata's model has been shown to differ from the optimal empirical model appropriate for such an environment. In this paper, propagation parameters, such as distance between transmitting and receiving antennas, transmitting power and terrain elevation, using sea level as reference point, were used as inputs to Artificial Neural Network (ANN) for the development of an ANN based path loss model. Data were acquired in a drive test through selected rural and suburban routes in Minna and environs as dataset required for training ANN model. Multilayer perceptron (MLP) network parameters were varied during the performance evaluation process, and the weight and bias values of the best performed MLP network were extracted for the development of the ANN based path loss models for two different routes, namely rural and suburban routes. The performance of the developed ANN based path loss model was compared with some of the existing techniques and modified techniques. Using Root Mean Square Error (RMSE) obtained between the measured and the model outputs as a measure of performance, the newly developed ANN based path loss model performed better than the basic empirical path loss models considered such as: Hata; Egli; COST-231; Ericsson models and modified path loss approach.


Julia Ofure Eichie, Onyedi David Oyedum, Moses Ajewole, and Abiodun Musa Aibinu, "Comparative Analysis of Basic Models and Artificial Neural Network Based Model for Path Loss Prediction," Progress In Electromagnetics Research M, Vol. 61, 133-146, 2017.


    1. Reddy, B. M., "Physics of the troposphere," Handbook on Radio Propagation for Tropical and Subtropical Countries, URSI Committee on Developing Countries, UNESCO Subvention, 59-77, New Delhi, 1987.

    2. Isabona, J., C. C. Konyeha, C. B. Chinule, and G. P. Isaiah, "Radio field strength propagation data and path loss calculation methods in UMTS network," Advances in Physics Theories and Applications, Vol. 21, 54-68, 2013.

    3. Ekpenyong, M., S. Robinson, and J. Isabona, "Macrocellular propagation prediction for wireless communications in urban environments," JCS & T, Vol. 10, No. 3, 130-136, 2010.

    4. Faruk, N., A. Ayeni, and Y. A. Adediran, "On the study of empirical pathloss models for accurate prediction of Tv signal for secondary users," Progress In Electromagnetics Research B, Vol. 49, 155-176, 2013.

    5. Nwalozie, G. C., S. U. Ufoaroh, C. O. Ezeagwu, and A. C. Ejiofor, "Pathloss prediction for GSM mobile networks for urban region of Aba, South-East, Nigeria," International Journal of Computer Science and Mobile Computing, Vol. 3, No. 2, 267-281, 2014.

    6. Bakinde, N. T., N. Faruk, A. A. Ayeni, M. Y. Muhammad, and M. I. Gumel, "Comparison of propagation models for GSM 1800 and WCDMA systems in selected urban areas of Nigeria," International Journal of Applied Information Systems (IJAIS), Vol. 2, No. 7, 6-13, 2012.

    7. Deligiorgi, D., K. Philippopoulos, and G. Kouroupetroglou, "Artificial neural network based methodologies for the spatial and temporal estimation of air temperature," International Conference on Pattern Recognition Applications and Methods, 669-578, 2013.

    8. Usman, A. U., O. U. Okereke, and E. E. Omizegba, "Instantaneous GSM signal strength variation with weather and environmental factors," American Journal of Engineering Research (AJSER), Vol. 4, No. 3, 104-115, 2015.

    9. Sharma, P. K. and R. K. Singh, "Comparative analysis of propagation path loss," International Journal of Engineering Science and Technology, Vol. 2, No. 6, 2008-2013, 2010.

    10. Ayekomilogbon, O., O. Famoriji, and O. Olasoji, "UHF band radio wave propagation mechanism in forested environments for wireless communication systems," Journal of Information Engineering and Applications, Vol. 3, No. 7, 11-16, 2013.

    11. Nwawelu, U. N., A. N. Nzeako, and M. A. Ahaneku, "The limitations of campus wireless networks: A case study of University of Nigeria, Nsukka," International Journal of Networks and Communications, Vol. 2, No. 5, 112-122, 2012.

    12. Ogbulezie, J. C., M. U. Onuu, D. E. Bassey, and S. Etienam-Umoh, "Site specific measurements and propagation models for GSM in three cities in Northern Nigeria," American Journal of Scientific and Industrial Research, Vol. 4, No. 2, 238-245, 2013a.

    13. Ogbulezie, J. C., M. U. Onuu, J. O. Ushie, and B. E. Usibe, "Propagation models for GSM 900 and 1800 MHz for Port Harcourt and Enugu, Nigeria," Network and Communication Technologies, Vol. 2, No. 2, 1-10, 2013b.

    14. Chebil, J., A. K. Lwas, M. R. Islam, and A. Zyoud, "Investigation of path loss models for mobile communications in Malaysia," Australian Journal of Basic and Applied Sciences, Vol. 5, No. 6, 365-371, 2011.

    15. Armoogum, V., R. Munee, and S. Armoogum, "Path loss analysis for 3G mobile networks for urban and rural regions of Mauritius," Proceedings of the Sixth International Conference on Wireless and Mobile Communications (ICWMC), 164-169, 2010.

    16. Benmus, T. A., R. Abboud, and M. K. Shater, "Neural network approach to model the propagation path loss for great Tripoli area at 900, 1800 and 2100 MHz bands," International Journal of Sciences and Techniques of Automatic Control and Engineering, Vol. 10, No. 2, 2121-2126, 2016.

    17. Obot, A., O. Simeon, and J. Afolayan, "Comparative analysis of path loss prediction models for urban macrocellular environments," Nigerian Journal of Technology, Vol. 30, No. 3, 50-59, 2011.

    18. Seybold, J. S., Introduction to RF Propagation, John Wiley & Sons Inc., New York, 2005.

    19. Rappaport, T. S., Wireless Communications: Principles and Practice, 2nd Ed., Prentice Hall, Upper Saddle River, New Jersey, USA, 2002.

    20. Ajose, S. O. and A. I. Imoize, "Propagation measurements and modelling at 1800 MHz in Lagos Nigeria," International Journal of Wireless and Mobile Computing, Vol. 6, No. 2, 154-173, 2013.

    21. Saunders, S. and A. Aragón-Zavala, Antennas and Propagation for Wireless Communication Systems, 2nd Ed., John Wiley & Sons Inc., New York, 2007.

    22. Milanovic, J., S. Rimac-Drlje, and I. S. Majerski, "Radiowave propagation mechanisms and empirical models for fixed wireless access systems," Technical Gazette, Vol. 17, No. 1, 43-52, 2010.

    23. Beale, M. H., M. T. Hagan, and B. O. Howard, Neural Network ToolboxTM, User Guide, Vol. 7, R2011b, 2011.

    24. Aibinu, A. M., A. A. Shafie, and M. J. Salami, "Performance analysis of ANN based YCbCr skin detection algorithm," Procedia Engineering, Vol. 41, 1183-1189, 2012.

    25. Eichie, J. O., O. D. Oyedum, M. O. Ajewole, and A. M. Aibinu, "Artificial neural network model for the determination of GSM rxlevel from atmospheric parameters," Engineering Science and Technology, retrieved from http://dx.doi.org/10.1016/j.jestch.2016.11.002, 2016.

    26. Ibeh, G. F. and G. A. Agbo, "Estimation of tropospheric refractivity with artificial neural network at Minna, Nigeria," Global Journal of Science Frontier Research Interdiciplinary, Vol. 2, No. 1, 8-14, 2012.

    27. Litta, A. J., S. M. Idicula, and U. C. Mohanty, "Artificial neural network model in prediction of meteorological parameters during premonsoon thunderstorms," International Journal of Atmospheric Sciences, Vol. 10, 1-14, 2013.

    28. Philippopoulos, K. and D. Deligiorgi, "Application of artificial neural networks for the spatial estimation of wind speed in a coastal region with complex topography," Renewable Energy, Vol. 39, 75-82, 2012.