Paper Info
Title
Channel Estimation In Unknown Noise: Application Of Canonical Correlation Decomposition In Subspaces
Abstract
The popular subspace algorithm proposed by Mouline et al. performs well when the channel output is corrupted by white noise. However, when the channel noise is correlated as is often encountered in practice, the standard subspace method degrades in performance. In this paper, based on second-order statistics and utilizing Canonical Correlation Decomposition (CCD) to obtain the subspaces, we develop two algorithms to blindly estimate the FIR channels in spatially correlated Gaussian noise with unknown covariance matrix. Our algorithms perform well in any unknown Gaussian noise environment and outperform existing methods proposed for similar scenarios.
Year
DOI
Venue
2005
10.1109/ISSPA.2005.1580978
ISSPA 2005: THE 8TH INTERNATIONAL SYMPOSIUM ON SIGNAL PROCESSING AND ITS APPLICATIONS, VOLS 1 AND 2, PROCEEDINGS
Keywords
Field
DocType
covariance matrix,degradation,canonical correlation,gaussian noise,white noise,statistics,application software
Value noise,Noise measurement,Subspace topology,Pattern recognition,Computer science,White noise,Artificial intelligence,Covariance matrix,Gaussian noise,Additive white Gaussian noise,Gradient noise
Conference
Citations 
PageRank 
References 
1
0.35
5
Authors
2
Name
Order
Citations
PageRank
Xiaojuan He1257.75
K.M. Wong21459147.00