Comparative Study of Least Square Methods for Tuning CCIR Pathloss Model

Abstract

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.

Assessment of Error Bounds for Path Loss Prediction Models for TV White Space Usage in Ekiti State, Nigeria

“…The selected models are, CCIR (developed by the Consultative Committee on International Radio) [4,5], COST 231(developed by the European Co-operative for Scientific and Technical Research) [6], ECC 33 (developed by the Electronic Communication Committee) [7], Free Space Path Loss (FSPL) [8,9], Hata [10], Hata-Davidson [11,12], Okumura [13] and the Plane Earth [14] model. This paper is organized in this order; Section I provides introduction; related works are presented in section II; Section III describes the method of data collection and research location; Models for path loss prediction were presented in Section IV; Section V presents data analysis and results; finally, Section VI compared the results of the selected models and proposed possible adjustments to the model with the best fit and concludes the paper.…”