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.
Highlights
Pathloss models are mathematical expressions designed for predicting the expected pathloss that signal can experience in a given environment [1,2,3,4,5,6]
The table shows that the untuned CCIR model has root mean square error (RMSE) of 17.33 dB and prediction accuracy of 85.33%
The pathloss predicted by the RMSE tuned CCIR model has RMSE of 4.09dB and prediction accuracy of 96.82%.and the pathloss predicted by the composition function tuned CCIR model has RME of 2.15 dB and prediction accuracy of 98.39%
Summary
Pathloss models are mathematical expressions designed for predicting the expected pathloss that signal can experience in a given environment [1,2,3,4,5,6]. Empirical pathloss models are the pathloss models that are developed based on empirical measurements conducted in a specific area [7,8,9]. Empirical pathloss models are limited in their ability to predict pathloss effectively in different environments other than the one where they are developed from [10,11,12,13,14]. The two approaches are basically least square methods that use different correction factors to minimize the pathloss prediction error. The correction factor is the root mean square error (RMSE)
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