Mobile radio propagation path loss prediction using Artificial Neural Networks with optimal input information for urban environments

Abstract

The propagation of radio waves in a built-up area is of great importance for the design of a mobile communication network and the Path Loss (PL) of the transmitted power is one of the characteristic parameters of the space channels. Traditional methods applied for the estimation of the PL are theoretical and empirical models proposed and well known in the literature. The Artificial Neural Network (ANN) methodology has been introduced as an alternative method for the PL prediction and has been proved effective in finding, via a stochastic evolutionary procedure, the influence of the configuration of the built up urban environment to the signal attenuation during its propagation through it. In most of the ANNs applied for this purpose general parameters, as the mean values of geometrical characteristics of roads and built blocks are used. In this paper the research has focused on the synthesis of ANNs which could obtain PL prediction of sufficient accuracy, using small amount but of proper kind input data. Analytical results are presented, which compared with respective ones received via theoretical methods, prove that the proposed ANNs technique with the optimal input information, is effective in estimating the power PL of the transmitted signals.