Empirical pathloss characterization for Oman

This paper attempts to provide some insight into the nature of radio propagation in that part of the spectrum (UHF) used by experimenters. As we know that the need for high quality and high capacity networks and estimating coverage accurately has become extremely important. Therefore, for more accurate design coverage of modern cellular networks, signal strength measurements must be taken in consideration, in order to provide an efficient and reliable coverage area. This paper addresses the applicability of Okumura-Hata model in Al Khuwair, Oman in GSM frequency band of 890-960 MHz. The study was carried out for urban area only, since measurements provided from local service provider were about the urban areas. The mean square error (MSE) was calculated between measured path loss values and those predicated on basis of Okumura-Hata model for this area. We accomplish the modification of model by investigating the variation in pathloss between the measured and predicted values, according to the Okumura-Hata propagation model for a cell in Al-Khuwair city and then finding the missing experimental data with spline and cubic regression. The selected model was considered the most suitable after initial study of several other models (not the scope of current study). The mean square error (MSE) was calculated between measured path loss values and those predicated on basis of Okumura-Hata model for an urban area. The MSE is up to 6dB, which is an acceptable value for the signal prediction. The obtained experimental data is compared and analyzed further using a cubic regression on the set of the experimental data. Scatter plot of the experimental data on path loss verses distance reveals a third order polynomial trend in the experimental data. Therefore the cubic regression model was fitted using method of least squares which estimates the parameters by minimizing sum of squares of the white noise. The coefficient of determination of this regression suggested that more than - 0% variation in path loss can be explained.