A comparison of neural network models for indoor field strength prediction

Abstract

This paper presents a comparison of the field strength prediction in indoor environments based on ray tracing, multilayer perceptron and radial basis function networks. It has been already shown for neural networks as powerful tool in RF propagation prediction. It is very important to choose proper algorithm for training a neural network, so we compared several training algorithms for the case of multilayer perceptron model. As the case used a corridor of university building in Dubrovnik, for which calculation, simulation and measurement of signal strength were obtained. The results show an improvement in field strength prediction with neural models over conventional models if training algorithm and neural network architecture are carefully chosen. The best results are obtained by the radial basis function neural network model.