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Parameter Estimation and Optimal Detection in Generalized Gaussian Noise

  • Author / Creator
    Guo, Qintian
  • 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.

  • Subjects / Keywords
  • Graduation date
    2014-06
  • Type of Item
    Thesis
  • Degree
    Master of Science
  • DOI
    https://doi.org/10.7939/R37H1DV8M
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
  • Language
    English
  • Institution
    University of Alberta
  • Degree level
    Master's
  • Department
    • Department of Electrical and Computer Engineering
  • Specialization
    • Communications
  • Supervisor / co-supervisor and their department(s)
    • Dr. Ying Tsui (Electrical and Computer Engineering)
    • Dr. Norman C. Beaulieu (Electrical and Computer Engineering)
  • Examining committee members and their departments
    • Mrinal Mandal (Electrical and Computer Engineering)
    • Hai Jiang (Electrical and Computer Engineering)
    • Majid Khabbazian (Electrical and Computer Engineering)