Paper Info
Title
Group decorrelation enhanced subspace method for identifying FIR MIMO channels driven by unknown uncorrelated colored sources
Abstract
Identification of finite-impulse-response (FIR) and multiple-input multiple-output (MIMO) channels driven by unknown uncorrelated colored sources is a challenging problem. In this paper, a group decorrelation enhanced subspace (GDES) method is presented. The GDES method uses the idea of subspace decomposition and signal decorrelation more effectively than the joint diagonalization enhanced subspace (JDES) method previously reported in the literature. The GDES method has a much better performance than the JDES method. The correctness of the GDES method is proved assuming that 1) the channel matrix is irreducible and column reduced and 2) the source spectral matrix has distinct diagonal functions. However, the GDES method has an inherent ability to trade off between the required condition on the channel matrix and that on the source spectral matrix. Simulations show that the GDES method yields good results even when the channel matrix is not irreducible, which is not possible at all for the JDES method.
Year
DOI
Venue
2005
10.1109/TSP.2005.859339
IEEE Transactions on Signal Processing
Keywords
DocType
Volume
jdes method,group decorrelation enhanced subspace,channel matrix,joint diagonalization enhanced subspace,source spectral matrix,subspace method,gdes method yield,subspace decomposition,fir mimo channel,better performance,gdes method,signal decorrelation,deconvolution,engineering,mathematical models,computer simulation,blind deconvolution,machine learning,system identification,learning artificial intelligence,signal processing,finite impulse response,adaptive signal processing
Journal
53
Issue
ISSN
Citations 
12
1053-587X
15
PageRank 
References 
Authors
0.68
8
4
Name
Order
Citations
PageRank
Senjian An130826.74
Yingbo Hua22614250.12
J.H. Manton31175.12
Zheng Fang4205.13