DMCA
Coarse Graining Monte Carlo Methods for Wireless Channels and Stochastic Differential Equations
BibTeX
@MISC{_coarsegraining,
author = {},
title = {Coarse Graining Monte Carlo Methods for Wireless Channels and Stochastic Differential Equations},
year = {}
}
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Abstract
ii This thesis consists of two papers considering different aspects of stochastic process modelling and the minimisation of computational cost. In the first paper, we analyse statistical signal properties and develop a Gaussian process model for scenarios with a moving receiver in a scattering environment, as in Clarke’s model, with the generalisation that noise is intro-duced through scatterers randomly flipping on and off as a function of time. The Gaussian process model is developed by extracting mean and covariance properties from the Multipath Fading Channel model (MFC) through coarse graining. That is, we verify that under certain assumptions, signal realisations of the MFC model converge to a Gaussian process and thereafter compute the Gaussian process ’ covariance matrix, which is needed to construct Gaussian pro-cess signal realisations. The obtained Gaussian process model is under certain assumptions less computationally costly, containing more channel information
Keyphrases
coarse graining monte carlo method stochastic differential equation wireless channel gaussian process model certain assumption gaussian process signal realisation channel information multipath fading channel model mfc model converge statistical signal property different aspect stochastic process modelling coarse graining obtained gaussian process model first paper computational cost clarke model gaussian process covariance matrix scattering environment covariance property gaussian pro-cess signal realisation
