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Electrical & Computer Engineering
Master of Science in Electrical and Computer Engineering (M.S.E.C.E.)
Year Degree Awarded
Month Degree Awarded
Radiometric identification, Volterra series, GLRT, Breaking anonymity, process variations, information theoretic
Breaking the anonymity of different wireless users with the purpose of decreasing internet crime rates is addressed in this thesis by considering radiometric identification techniques.
Minute imperfections and non-idealities in the different transmitter components, especially the inherent nonlinearity in power amplifiers, result in variations in their Volterra series representations which could be utilized as a signature.
For a two user scenario, signal processing algorithms based on generalized likelihood ratio test(GLRT) and the classical likelihood ratio test are introduced and the resulting receiver decision rules and performance curves are presented. These algorithms consider the high signal to noise ratio(SNR) case where we have available the input samples completely at the receiver which is a practical assumption for most cases.
Volterra series are widely used in behavioral modeling of power amplifiers. To validate the existence of these variations in the Volterra series representation of power amplifiers, process variations are introduced as major sources. The plausibility of our techniques are justified by deriving and comparing the Volterra coefficients for the fast and slow process corners.
Finally,an information theoretic framework is presented where the amount of mutual information of the output about the Volterra coefficients represents the amount of anonymity taken from users. Here, some results for the low SNR case are presented to prove the achievability of some information about individual systems using our hardware anonymity breaking techniques.
Dennis L. Goeckel