1. Characterization of Pathloss Using Okumura-Hata Model and Missing Data Prediction for Oman.
- Author
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Nadir, Zia and Ahmad, Muhammad Idrees
- Subjects
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RADIO wave propagation , *WIRELESS communications , *GSM communications , *RADIO frequency , *REGRESSION analysis , *SIMULATION methods & models - Abstract
In this chapter we aim to adapt a propagation model for Salalah city (Oman) as we examine the applicability of Okumura-Hata model in Oman in GSM frequency band. The study is a part of an ongoing work by authors, which is being carried out for an urban area on the data obtained from local operator. We accomplished the modification of the model by investigating the variation in pathloss between the measured and predicted values, according to the Okumura-Hata propagation model for a cell in Salalah city and then finding the missing experimental data with cubic regression and spline interpolation. We also verified it by applying the model for other cells. The mean square error (MSE) was calculated between measured path loss values and those predicated on the basis of Okumura-Hata model for an open area. The MSE went up to 6dB, which is an acceptable value for the signal prediction. The model showed a significant difference in an open area that allowed necessary changes to be introduced in the model. Modified equation was also re-verified for another new cell in an open area which gave acceptable results. Theoretical simulation by Okumura-Hata Model and 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 pathloss verses distance reveals a third order polynomial trend in the experimental data. Therefore, the cubic regression model was used to estimate the parameters by minimizing the sum of squares of the white noise. The coefficient of determination of this regression suggested that about 90% variation in pathloss can be explained. Theoretical simulation by Okumura-Hata Model and the obtained experimental data is compared and analyzed further using a piece-wise cubic spline to interpolate the set of the experimental data and finding the missing experimental data points. [ABSTRACT FROM AUTHOR]
- Published
- 2010
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