Free Deconvolution for Signal Processing Applications

Author

Ryan, O. ; Debbah, M.

Author_Institution

Univ. of Oslo, Oslo

fYear

2007

fDate

24-29 June 2007

Firstpage

1846

Lastpage

1850

Abstract

Situations in many fields of research, such as digital communications, nuclear physics and mathematical finance, can be modelled with random matrices. When the matrices get large, free probability theory is an invaluable tool for describing the asymptotic behaviour of many systems. It will be explained how free probability can be used to estimate covariance matrices. Multiplicative free deconvolution is shown to be a method which can aid in expressing limit eigenvalue distributions for sample covariance matrices, and to simplify estimators for eigenvalue distributions of covariance matrices.

Keywords

covariance matrices; deconvolution; eigenvalues and eigenfunctions; estimation theory; statistical distributions; covariance matrix estimation; free probability theory; limit eigenvalue distributions; multiplicative free deconvolution; random matrices; signal processing applications; Convolution; Covariance matrix; Deconvolution; Digital communication; Eigenvalues and eigenfunctions; Finance; Informatics; Nuclear physics; Sensor phenomena and characterization; Signal processing; Free Probability Theory; G-analysis; Random Matrices; deconvolution; limiting eigenvalue distribution;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Theory, 2007. ISIT 2007. IEEE International Symposium on

Conference_Location

Nice

Print_ISBN

978-1-4244-1397-3

Type

conf

DOI

10.1109/ISIT.2007.4557490

Filename

4557490