Let the column vectors of X: M\_N, M<N, be distributed as independent complex normal vectors with the same covariance matrix 7. Then the usual quadratic form in the complex normal vectors is denoted by Z=XLX H where L: N\_N is a positive definite hermitian matrix. This paper deals with a representat
A Determinant Representation for the Distribution of a Generalised Quadratic Form in Complex Normal Vectors
β Scribed by Peter J Smith; Hongsheng Gao
- Publisher
- Elsevier Science
- Year
- 2000
- Tongue
- English
- Weight
- 186 KB
- Volume
- 73
- Category
- Article
- ISSN
- 0047-259X
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β¦ Synopsis
Consider the quadratic form Z=Y H (XL X H ) &1 Y where Y is a p_m complex Gaussian matrix, X is an independent p_n complex Gaussian matrix, L is a Hermitian positive definite matrix, and m p n. The distribution of Z has been studied for over 30 years due to its importance in certain multivariate statistics but no satisfactory numerical methods for computing this distribution appear to be available. Hence this paper deals with a representation for the density function of Z in terms of a ratio of determinants which is shown to be more amenable to numerical work than previous representations, at least for small values of p. Also for m=1 this work has applications in digital mobile radio for a specific channel where p antennas are used to receive a signal with n interferers. Some of these applications in radio communication systems are discussed.
π SIMILAR VOLUMES
In this paper a series representation of the joint density and the joint distribution of a quadratic form and a linear form in normal variables is developed. The expansion makes use of Laguerre polynomials. As an example the calculation of the joint distribution of the mean and the sample variance i