Connections between the least-squares and the subspace approaches to blind channel estimation

Author

Zeng, Hanks H. ; Tong, Lang

Author_Institution

Dept. of Electr. & Syst. Eng., Connecticut Univ., Storrs, CT, USA

Volume

44

Issue

6

fYear

1996

fDate

6/1/1996 12:00:00 AM

Firstpage

1593

Lastpage

1596

Abstract

In this correspondence, we study the connections between the least-squares and the subspace approaches to blind channel estimation. By examining the properties and connections of the so-called multichannel filtering and data selection transforms, we establish a relationship between the identification equations used in the two approaches. Next, it is shown that the least-squares and subspace estimators are identical for the case when there are two subchannels. In general, the two algorithms are different in their utilization of the noise subspace

Keywords

filtering theory; interference (signal); least squares approximations; noise; parameter estimation; transforms; algorithms; blind channel estimation; connections; data selection transforms; identification equations; least-squares approach; multichannel filtering; noise subspace; properties; subchannels; subspace approach; subspace estimators; Blind equalizers; Covariance matrix; Equations; Filtering; Monitoring; Performance gain; Systems engineering and theory; Transforms; Vectors;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/78.506629

Filename

506629