Adaptation of Propagation Model Parameters toward Efficient Cellular Network Planning using Robust LAD Algorithm

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Isabona Joseph 1,* Divine O. Ojuh 2

1. Department of Physics, Federal University Lokoja, Kogi State, Nigeria

2. Department of Physical Sciences, Benson Idahosa University, Benin City, Nigeria

* Corresponding author.


Received: 16 Jun. 2020 / Revised: 2 Jul. 2020 / Accepted: 20 Jul. 2020 / Published: 8 Oct. 2020

Index Terms

Propagation loss, Adaptive propagation model tuning, Least square, Least absolute deviation.


All new mobile radio communication systems undergo a cautious cellular network planning and re-planning process in order to resourcefully utilize the allotted frequency band and also ensure that the geographical area of focus is adequately fortified with integrated base stations transmitters. To this end, efficient radio propagation model prediction and tuning is of huge importance, as it assists radio network engineers to effectively assess and plan the cellular network signal coverage area. In this research work, an adaptive least absolute deviation approach is proposed and verified to fine-tune the parameters of Ericsson propagation model. The adaptive tuning technique have been verified experimentally with field propagation loss data acquired over three different suburban locations of a recently deployed LTE radio cellular network in Waterlines area of Port Harcourt City. In terms of the mean absolute percentage error and coefficient of efficiency, the outcomes of the proposed adaptive tuning approach show a higher degree of prediction performance accuracy on the measured loss data compared to the commonly applied least squares regression tuning technique.

Cite This Paper

Isabona Joseph´╝î Divine O. Ojuh, " Adaptation of Propagation Model Parameters toward Efficient Cellular Network Planning using Robust LAD Algorithm", International Journal of Wireless and Microwave Technologies(IJWMT), Vol.10, No.5, pp. 13-24, 2020. DOI: 10.5815/ijwmt.2020.05.02


[1]Isabona J., Srivastava V. M. Coverage and Link Quality Trends in Suburban Mobile Broadband HSPA Network Environments. Wireless personal Communications, 95, 3955–3968, 2017. 

[2]Mardeni R., Lee Y. P. Y. P. The Optimization of Okumura’s Model for Code Division Multiple Access (CDMA) System in Malaysia. European Journal of Scientific Research, 45 (4), 508-528, 2010. 

[3]AarnæS E., Holm S. Tuning of empirical radio propagation models effect of location accuracy. Wireless Personal Communications, 30 (2-4), 267–281, 2004.

[4]Chimaobi N.N., Nnadi C.C., Nzegwu A.J. Comparative Study of Least Square Methods for Tuning Erceg  Pathloss Model. American Journal of Software Engineering and Applications. 6 (3), 61-66, 2017. 

[5]Isabona J., Konyeha C.C. Urban Area Path loss Propagation Prediction and Optimisation Using Hata Model at 800MHz. IOSR Journal of Applied Physics (IOSR-JAP), 3 (4), 8-18, 2013. 

[6]Castro B. S., Pinheiro M. R., Cavalcante G. P., Gomes I. R., Carneiro O. d. O. Comparison between known propagation models using least squares tuning algorithm on 5.8 GHz in Amazon region cities. Journal of Microwaves, Optoelectronics and Electromagnetic Applications, 10 (1), 106-113, 2011.

[7]Isabona J., Azi S. Optimised Walficsh-Bertoni Model for Pathloss Prediction in Urban Propagation Environment. International Journal of Engineering and Innovative Technology (IJEIT), 2 (5), 14-20, 2012.

[8]Castro-Hernandez D., Paranjape R. Local Tuning of a Site-Specific Propagation Path Loss Model for Microcell Environments, International Journal of Wireless Personal communications, 91 (2), 709-728, 2016.

[9]Liming X., Dacheng Y. A Recursive Algorithm for Radio Propagation Model Calibration based on CDMA Forward Pilot Channel, In Proceedings of IEEE 14th International Symposium on Personal, Indoor and Mobile Radio Communication, 970-972, 2003.

[10]Mingjing Yang., Wenxiao Shi. A linear least square method of propagation model tuning for 3G radio network planning. In Fourth International Conference on Natural Computation, 5150–154, 2008.

[11]Mohammed A.K., Jaafar A.A. Performance Evaluation of Path Loss in Mobile Channel for Karada Distric in Baghdad City. Engineering and Technical Journal, 30 (17), 3023-3038, 2012.

[12]Simic I. L., Stanic I.,  Zrnic B. Minimax LS algorithm for automatic propagation model tuning, Proceeding of the 9th Telecommunications Forum, Belgrade, Serbia Nov. 20-22, , 2001, 1-5. 

[13]Ravindra K., Sarma A.D., Prasad M. V. S. N., An Adaptive Polynomial Path Loss Model at UHF frequencies for Mobile railway Communications. Indian Journal of Radio and Space Physics, 31, 278-284.

[14]Huber P.J., Ronchetti E. M. Robust Statistics”, second edition John Wiley & Sons, Inc., publication, 2009. 

[15]Schumacker R.E., Monahan M.P., Mount R. E., A comparison of OLS and robust regression using S-PLUS. Multiple Linear Regression Viewpoints, 28(2), 10-13, 2002. 

[16]Ericsson Radio Systems AB, TEMS Cell Planner 3.4 User Guide, 2001.

[17]Milanovic J., Rimac-Drlje. S.  Bejuk K. Comparison of propagation model accuracy for WiMAX on 3.5GHz. Proceedings of the 14th IEEE, International Conference on Electronic Circuits and Systems, Dec. 11-14, 2007. IEEE Xplore Press, Morocco, 111-114. DOI: 10.1109/ICECS.2007.4510943 

[18]Fratu O., Martian A., Craciunescu R., Vulpe A., Halunga S., Zaharis Z., Lazaridis P., Kasampalis S. Comparative study of Radio Mobile and ICS Telecom propagation prediction models for DVB-T. In IEEE BMSB 2015 International Conference, 17th - 19th June 2015, Ghent, Belgium.

[19]Isabona J., Isaiah G.P. Computation and Verification of Propagation Loss Models based on Electric Field Data in Mobile Cellular Networks, Australian Journal of Basic and Applied Sciences,  9(31), 280-285, 2015  .

[20]Isabona, J. Wavelet Generalized Regression Neural Network Approach for Robust Field Strength Prediction, Wireless Personal Communication (Springer), 2020.