Gaussian Coarse Graining of a Master Equation Extension of Clarke's Model

We study the error and computational cost of generating outputsignal realizations for the channel model of a moving receiver in a scatteringenvironment, as in Clarke’s model, with the extension that scatterers randomlyflip on and off. At micro scale, the channel is modeled by a Multipath FadingChannel (MFC) model, and by coarse graining the micro scale model we derivea macro scale Gaussian process model. Four algorithms are presented for gen-erating stochastic signal realizations, one for the MFC model and three for theGaussian process model. A computational cost comparison of the presentedalgorithms indicates that Gaussian process algorithms generate signal realiza-tions more efficiently than the MFC algorithm does. Numerical examples ofgenerating signal realizations in time independent and time dependent scatter-ing environments are given, and the problem of estimating model parametersfrom real life signal measurements is also studied.Funded by Centre for Industrial and Applied Mathematics (CIAM). QC 2012050