Neural network approach to model the propagation path loss for great Tripoli area at 900, 1800, and 2100 MHz bands

Radio wave propagation models are extremely important in mobile network planning and design since it used to predict the Received Signal Strength (RSS). In this work an empirical model was develop to predicate the propagation path loss at the capital of Libya “Tripoli”, based on quit good number of measurements conducted in different places in the target area using the Neural Network approach. This model is very helpful in designing a cellular network in this area and other places having the same environments. The work was done based on real measurements were the RSS conducted from 0 to 1 km distance range in the concerned area at three different frequency bands; 900 MHz, 1800 MHz, and 2100 MHz The measurements were collected in five types of areas; Dense Urban, Urban, Dense Suburban, Suburban and Rural. The proposed model was tested and gives an acceptable accuracy results. The values of RSS obtained from this model were compared with other values obtained from applying the Hata model. It has been found that the results of this work are much closer to the real measurement data and gives 7.1 to 28.8 dB improvements in the accuracy over the Hata model results. The Means Square Error (MSE) was found between 3 to 6.7 for the proposed model.