Error bounds of empirical path loss models at VHF/UHF bands in Kwara State, Nigeria

Propagation models are used extensively in signal prediction and interference analysis. Peculiarity of these models gives rise to high prediction errors when deployed in a different environment other than the one initially built for. In this paper, we provide the error bounds on the efficacy at pred...

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Bibliographic Details
Main Authors: Faruk, Nasir, Adediran, Yinusa A., Ayeni, Adeseko A.
Format: Conference Proceeding
Language:English
Subjects:
Analytical models
Buildings
Cities and towns
Davidson model
Error Bound
Frequency measurement
Hata model
Loss measurement
Path loss
Predictive models
Propagation losses
Propagation model
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Summary:Propagation models are used extensively in signal prediction and interference analysis. Peculiarity of these models gives rise to high prediction errors when deployed in a different environment other than the one initially built for. In this paper, we provide the error bounds on the efficacy at predicting path loss for 10 empirical widely used path loss models. The results show that no single model provides a good fit consistently. However, Hata and Davidson models provide good fitness along some selected routes with measured RMSE values of less than 8 dB. ITU-R P.1546-3, Walfisch Ikegami, ECC, Egli, CCIR and FSPL perform woefully, with higher RMSE and SC-RMSE (Spread Corrected RMSE) values. In terms of mean value errors, Hata, Davidson and ITU-R P.5293 models give mean values close to zero. However, COST 231 also provides better skew, while CCIR and ECC gives fair results but ITU-R P. 1546, WI and FSPL give worst results. The prediction errors for Davidson are nearly distributed symmetrically around the mean error of 2.15 dB. It was also observed that, the Gaussian error distribution within the window of ±5 dB dominates the frequency counts. However, the error counts for CCIR model are quite high but spread along the distribution but HATA, FSPL, Walfisch Ikegami and ITU-R P. 5293 models does not follow normal distribution.
DOI:10.1109/EUROCON.2013.6625043