A globally convergent approach for blind MIMO adaptive deconvolution

Author

Touzni, Azzédine ; Fijalkow, Inbar ; Larimore, Michael G. ; Treichler, John R.

Author_Institution

ENSEA, Cergy-Pontoise Univ., France

Volume

49

Issue

6

fYear

2001

fDate

6/1/2001 12:00:00 AM

Firstpage

1166

Lastpage

1178

Abstract

We discuss the blind deconvolution of multiple input/multiple output (MIMO) linear convolutional mixtures and propose a set of hierarchical criteria motivated by the maximum entropy principle. The proposed criteria are based on the constant-modulus (CM) criterion in order to guarantee that all minima achieve perfectly restoration of different sources. The approach is moreover robust to errors in channel order estimation. Practical implementation is addressed by a stochastic adaptive algorithm with a low computational cost. Complete convergence proofs, based on the characterization of all extrema, are provided. The efficiency of the proposed method is illustrated by numerical simulations

Keywords

MIMO systems; adaptive signal processing; convergence of numerical methods; convolution; deconvolution; maximum entropy methods; signal restoration; stochastic processes; blind MIMO adaptive deconvolution; blind deconvolution; channel order estimation; constant-modulus criterion; efficiency; global convergence; hierarchical criteria; linear convolutional mixtures; low computational cost; maximum entropy principle; multiple input/multiple output; numerical simulations; source restoration; stochastic adaptive algorithm; Adaptive algorithm; Computational efficiency; Convergence; Convolution; Deconvolution; Entropy; Interference suppression; MIMO; Robustness; Stochastic processes;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/78.923299

Filename

923299