A new transform domain LMS algorithm

In this paper a Krylov subspace transform domain lease mean square (LMS) algorithm is proposed. The unknown system can be sparse after Krylov subspace transform, thus a much smaller tap length can be used for the update of adaptive filer coefficients in transform domain, which results in a significa...

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Bibliographic Details
Main Authors: Yonggang Zhang, Qiang Yu, Lei Wu, Ping Huang
Format: Conference Proceeding
Language:English
Subjects:
adaptive filter
Adaptive filters
Automation
Convergence
Discrete cosine transforms
Discrete Fourier transforms
Discrete transforms
Discrete wavelet transforms
Fourier transforms
Krylov subspace
Least squares approximation
LMS
Robustness
transform domain
variable tap length
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Summary:In this paper a Krylov subspace transform domain lease mean square (LMS) algorithm is proposed. The unknown system can be sparse after Krylov subspace transform, thus a much smaller tap length can be used for the update of adaptive filer coefficients in transform domain, which results in a significant improvement of convergence rate. The small tap length in transform domain can be found by using variable tap-length LMS algorithm. Simulation is performed to show the advantage of the proposed algorithm. As can be seen from simulation results, the proposed algorithm has an improved convergence rate as compared with the LMS algorithm.
DOI:10.1109/ICINFA.2010.5512291