Comparative Study of Least Square Methods for Tuning Erceg Pathloss Model
American Journal of Software Engineering and Applications
Volume 6, Issue 3, June 2017, Pages: 61-66
Received: Jan. 8, 2017; Accepted: Jan. 18, 2017; Published: Jun. 12, 2017
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Nnadi Nathaniel Chimaobi, Department of Electrical/Electronic Engineering Imo State Polytechnic, Umuagwo, Owerri, Nigeria
Charles Chukwuemeka Nnadi, Department of Electrical/Electronic Engineering Imo State Polytechnic, Umuagwo, Owerri, Nigeria
Amaechi Justice Nzegwu, Department of Science Laboratory Technology Imo State Polytechnic, Umuagwo, Owerri, Nigeria
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In this paper, a study of two least square error approaches for optimizing Erceg pathloss model is presented. The first approach is implemented by the addition of the root mean square error (RMSE) if the sum of prediction errors is positive otherwise, the RMSE is subtracted from the pathloss predicted by the original Erceg model. In the second method, the composition function of the residue is used to generate the model correction factor that is added to the original Erceg model pathloss prediction. The study is based on field measurement carried out in a suburban area for a GSM network in the 800 MHz frequency band. The results show that the untuned Erceg model has RMSE of 59.27384 dB and prediction accuracy of 59.57243%. On the other hand, the pathloss predicted by the RMSE tuned Erceg model has RMSE of 4.495422dB and prediction accuracy of 97.28188% and the pathloss predicted by the composition function tuned Erceg model has RME of 2.177523 dB and prediction accuracy of 98.7253%. In any case, the two methods are effective in minimizing the error to within the acceptable value of less than 7 dB. However, the composition function approach has better pathloss prediction performance with smaller RMSE and higher prediction accuracy than the RMSE-based approach.
Pathloss, Erceg Model, Least Square Method, Propagation Model, Model Tuning, Composition Function Of Residue
To cite this article
Nnadi Nathaniel Chimaobi, Charles Chukwuemeka Nnadi, Amaechi Justice Nzegwu, Comparative Study of Least Square Methods for Tuning Erceg Pathloss Model, American Journal of Software Engineering and Applications. Vol. 6, No. 3, 2017, pp. 61-66. doi: 10.11648/j.ajsea.20170603.11
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This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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