Title :
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
fDate :
6/1/1996 12:00:00 AM
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;
Journal_Title :
Signal Processing, IEEE Transactions on
