The blind deconvolution of the multi-channel based on the higher order statistics

Author

Yang, Janghoon ; Nikias, Chrysostomos L.

Author_Institution

Dept. of Electr. Eng. Syst., Univ. of Southern California, Los Angeles, CA, USA

Volume

2

fYear

2000

fDate

Oct. 29 2000-Nov. 1 2000

Firstpage

1192

Abstract

We have proposed a source separation algorithm of the convolutive mixtures based on the maximization of the auto-kurtosis and minimization of the cross-kurtosis with the constraint on the output power. As an iterative method, we suggest a nontrivial extension of the generalized eigenvector algorithm for blind equalization (GEnEVA) to the blind deconvolution of the multi-input multi-output (MIMO) systems. The application of the proposed algorithm on the 64-QAM signal separations shows that it can achieve excellent performance and it is robust to the broad range of the noise levels.

Keywords

MIMO systems; blind equalisers; deconvolution; eigenvalues and eigenfunctions; higher order statistics; iterative methods; minimisation; quadrature amplitude modulation; telecommunication channels; 64-QAM signal separation; GEnEVA algorithm; MIMO systems; auto-kurtosis maximization; blind equalization; blind multichannel deconvolution; convolutive mixtures; cross-kurtosis minimization; generalized eigenvector algorithm; higher order statistics; iterative method; multi-input multi-output systems; noise levels; output power constraint; source separation algorithm; Blind equalizers; Deconvolution; Iterative algorithms; Iterative methods; MIMO; Minimization methods; Noise level; Noise robustness; Power generation; Source separation;

fLanguage

English

Publisher

ieee

Conference_Titel

Signals, Systems and Computers, 2000. Conference Record of the Thirty-Fourth Asilomar Conference on

Conference_Location

Pacific Grove, CA, USA

ISSN

1058-6393

Print_ISBN

0-7803-6514-3

Type

conf

DOI

10.1109/ACSSC.2000.910752

Filename

910752