Diffraction Loss Prediction of Multiple Edges Using Bullington Method with Neural Network in Mountainous Areas

Abstract

This paper proposes a neural network approach to improve the Bullington method by using parameters obtained from ignored obstacles in mountainous areas. Measurements were performed in mountainous areas to compare the prediction accuracy of propagation loss. And the measured data were used for neural network training. A detailed description of the input parameters of the proposed neural network is presented. The prediction performances were improved by up to 3.20 dB in the average error and 2.11 dB in the standard deviation of errors by the proposed method when compared to traditional diffraction methods.