1 - Model selection

Abstract

This initial chapter shows how wireless channel modeling can be done when epistemic uncertainty may play a major role in the prediction of system performance. Although most of the tools described here can be adapted to different situations, we focus our attention on the derivation, based on incomplete knowledge available, of the probability density function of the random fading attenuation process affecting wireless communications. We proceed as follows:▪We first describe how a probability density function of a given family, whose form is known but whose parameters are unspecified, can be fitted to experimental data: Rayleigh, Rice, and Nakagami-m fading models.▪If only incomplete information about a random variable is available, and in particular its probability density function has an unknown form, we describe how the maximum-entropy method can be used to determine a probability density function consistent with what is known about the random variable.▪Assuming a given class of fading models in which the true model, or at least a good approximation to it, is believed to lie, we show how the Akaike Information Criterion can be used to make the best model choice.