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Permanent link (DOI): https://doi.org/10.7939/R37H1DV8M

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Parameter Estimation and Optimal Detection in Generalized Gaussian Noise Open Access

Descriptions

Other title
Subject/Keyword
Generalized Gaussian Noise
Optimal Detection
Parameter Estimation
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Guo, Qintian
Supervisor and department
Dr. Ying Tsui (Electrical and Computer Engineering)
Dr. Norman C. Beaulieu (Electrical and Computer Engineering)
Examining committee member and department
Majid Khabbazian (Electrical and Computer Engineering)
Hai Jiang (Electrical and Computer Engineering)
Mrinal Mandal (Electrical and Computer Engineering)
Department
Department of Electrical and Computer Engineering
Specialization
Communications
Date accepted
2014-03-04T10:56:20Z
Graduation date
2014-06
Degree
Master of Science
Degree level
Master's
Abstract
Modern signal processing algorithms need to work in complicated and variable noise environments. The generalized Gaussian distribution (GGD) can be used to accurately model noise in signal processing for telecommunication and other fields because the GGD covers a wide range of distributions. Three distributions widely used for the modeling of noise including the Laplace, Gaussian and uniform distributions are special cases of the GGD with the shape parameter p having values of 1, 2 and infinity respectively. In this thesis, estimation of the location parameter of the GGD is investigated. When the shape parameter p takes different values, three estimators are derived based on the maximum likelihood estimation theory. An optimal detector in the presence of generalized Gaussian distributed noise is proposed. The asymptotic performance of the optimal detector is analyzed by using the Gaussian approximation method.
Language
English
DOI
doi:10.7939/R37H1DV8M
Rights
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
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