Field strength prediction in indoor environments with neural networks

Abstract

A new model for the field strength prediction for mobile communication networks inside buildings is presented. The model is based on artificial neural networks, trained with measurements. In contrast to other neural prediction models a good generalization is achieved, so the prediction results are also very accurate in buildings not used for the training of the neural network. Two algorithms for the selection of the training patterns for the neural networks are presented and compared to each other.