Volume 5, Issue 3, May 2017, Page: 19-23
Comparative Study of Least Square Methods for Tuning CCIR Pathloss Model
Nnadi Nathaniel Chimaobi, Department of Electrical/Electronic Engineering, Imo State Polytechnic, Umuagwo, Owerri, Nigeria
Ifeanyi Chima Nnadi, Department of Electrical/Electronic Engineering, Imo State Polytechnic, Umuagwo, Owerri, Nigeria
Chibuzo Promise Nkwocha, Department of Chemical Engineering, Federal University of Technology, Owerri (FUTO), Owerri, Nigeria
Received: Jan. 8, 2017;       Accepted: Jan. 24, 2017;       Published: Jun. 14, 2017
DOI: 10.11648/j.com.20170503.11      View  1700      Downloads  98
Abstract
Comparative study of two least square methods for tuning CCIR pathloss model is presented. The first model tuning approach is implemented by the addition or subtraction of the root mean square error (RMSE) based on whether the sum of errors is positive or negative. The second method is implemented by addition of a composition function of the residue to the original CCIR model pathloss prediction. The study is based on field measurement carried out in a suburban area for a GSM network in the 1800 MHz frequency band. The results show that the untuned CCIR model has a root mean square error (RMSE) of 17.33 dB and prediction accuracy of 85.33%. On the other hand, the pathloss predicted by the RMSE tuned CCIR model has RMSE of 4.09dB and prediction accuracy of 96.82% while the pathloss predicted by the composition function tuned CCIR model has RME of 2.15 dB and prediction accuracy of 98.39%. In all, both 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.
Keywords
Pathloss, Propagation Model, CCIR Model, Composition Function, Empirical Model, RMSE-Based Tuning Approach, Least Square Method
To cite this article
Nnadi Nathaniel Chimaobi, Ifeanyi Chima Nnadi, Chibuzo Promise Nkwocha, Comparative Study of Least Square Methods for Tuning CCIR Pathloss Model, Communications. Vol. 5, No. 3, 2017, pp. 19-23. doi: 10.11648/j.com.20170503.11
Copyright
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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