A mathematical framework for expressing multivariate distributions useful in wireless communications

  • Author / Creator
    Hemachandra, Kasun Thilina
  • Multivariate statistics play an important role in performance analysis of wireless communication systems in correlated fading channels. This thesis presents a framework which can be used to derive easily computable mathematical representations for some multivariate statistical distributions, which are derivatives of the Gaussian distribution, and which have a particular correlation structure. The new multivariate distribution representations are given as single integral solutions of familiar mathematical functions which can be evaluated using common mathematical software packages. The new approach can be used to obtain single integral representations for the multivariate probability density function, cumulative distribution function, and joint moments of some widely used statistical distributions in wireless communication theory, under an assumed correlation structure. The remarkable advantage of the new representation is that the computational burden remains at numerical evaluation of a single integral, for a distribution with an arbitrary number of dimensions. The new representations are used to evaluate the performance of diversity combining schemes and multiple input multiple output systems, operating in correlated fading channels. The new framework gives some insights into some long existing open problems in multivariate statistical distributions.

  • Subjects / Keywords
  • Graduation date
    Fall 2010
  • Type of Item
  • Degree
    Master of Science
  • DOI
  • 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.