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This letter analyses the convergence behaviour of the transform domain least mean square (TDLMS) adaptive filtering algorithm which is based on a well known interpretation of the variable stepsize algorithm. With this interpretation, the analysis is considerably simplified. The time varying stepsize is implemented by the modified power estimator to redistribute the spread power after transformation. The main contribution of this letter is the statistical performance analysis in terms of mean and mean squared error of the weight error vector and the decorrelation property of the TDLMS is presented by the lower and upper bound of eigenvalue spread ratio. The theoretical analysis results are validated by Monte Carlo simulation.

- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E83-A No.4 pp.764-770

- Publication Date
- 2000/04/25

- Publicized

- Online ISSN

- DOI

- Type of Manuscript
- LETTER

- Category
- Digital Signal Processing

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Dai I. KIM, Philippe De WILDE, "Transform Domain Adaptive Filtering Algorithm via Modified Power Estimator" in IEICE TRANSACTIONS on Fundamentals,
vol. E83-A, no. 4, pp. 764-770, April 2000, doi: .

Abstract: This letter analyses the convergence behaviour of the transform domain least mean square (TDLMS) adaptive filtering algorithm which is based on a well known interpretation of the variable stepsize algorithm. With this interpretation, the analysis is considerably simplified. The time varying stepsize is implemented by the modified power estimator to redistribute the spread power after transformation. The main contribution of this letter is the statistical performance analysis in terms of mean and mean squared error of the weight error vector and the decorrelation property of the TDLMS is presented by the lower and upper bound of eigenvalue spread ratio. The theoretical analysis results are validated by Monte Carlo simulation.

URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e83-a_4_764/_p

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@ARTICLE{e83-a_4_764,

author={Dai I. KIM, Philippe De WILDE, },

journal={IEICE TRANSACTIONS on Fundamentals},

title={Transform Domain Adaptive Filtering Algorithm via Modified Power Estimator},

year={2000},

volume={E83-A},

number={4},

pages={764-770},

abstract={This letter analyses the convergence behaviour of the transform domain least mean square (TDLMS) adaptive filtering algorithm which is based on a well known interpretation of the variable stepsize algorithm. With this interpretation, the analysis is considerably simplified. The time varying stepsize is implemented by the modified power estimator to redistribute the spread power after transformation. The main contribution of this letter is the statistical performance analysis in terms of mean and mean squared error of the weight error vector and the decorrelation property of the TDLMS is presented by the lower and upper bound of eigenvalue spread ratio. The theoretical analysis results are validated by Monte Carlo simulation.},

keywords={},

doi={},

ISSN={},

month={April},}

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TY - JOUR

TI - Transform Domain Adaptive Filtering Algorithm via Modified Power Estimator

T2 - IEICE TRANSACTIONS on Fundamentals

SP - 764

EP - 770

AU - Dai I. KIM

AU - Philippe De WILDE

PY - 2000

DO -

JO - IEICE TRANSACTIONS on Fundamentals

SN -

VL - E83-A

IS - 4

JA - IEICE TRANSACTIONS on Fundamentals

Y1 - April 2000

AB - This letter analyses the convergence behaviour of the transform domain least mean square (TDLMS) adaptive filtering algorithm which is based on a well known interpretation of the variable stepsize algorithm. With this interpretation, the analysis is considerably simplified. The time varying stepsize is implemented by the modified power estimator to redistribute the spread power after transformation. The main contribution of this letter is the statistical performance analysis in terms of mean and mean squared error of the weight error vector and the decorrelation property of the TDLMS is presented by the lower and upper bound of eigenvalue spread ratio. The theoretical analysis results are validated by Monte Carlo simulation.

ER -