IEICE TRANSACTIONS on Fundamentals
Partial-Update Normalized Sign LMS Algorithm Employing Sparse Updates
Summary :
This paper provides a novel normalized sign least-mean square (NSLMS) algorithm which updates only a part of the filter coefficients and simultaneously performs sparse updates with the goal of reducing computational complexity. A combination of the partial-update scheme and the set-membership framework is incorporated into the context of L∞-norm adaptive filtering, thus yielding computational efficiency. For the stabilized convergence, we formulate a robust update recursion by imposing an upper bound of a step size. Furthermore, we analyzed a mean-square stability of the proposed algorithm for white input signals. Experimental results show that the proposed low-complexity NSLMS algorithm has similar convergence performance with greatly reduced computational complexity compared to the partial-update NSLMS, and is comparable to the set-membership partial-update NLMS.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E96-A No.6 pp.1482-1487
- Publication Date
- 2013/06/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E96.A.1482
- Type of Manuscript
- LETTER
- Category
- Digital Signal Processing
Authors
Seong-Eun KIM
Samsung Electronics
Young-Seok CHOI
Gangneung-Wonju National University
Jae-Woo LEE
Pohang University of Science and Technology (POSTECH)
Woo-Jin SONG
Pohang University of Science and Technology (POSTECH)
Keyword
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Seong-Eun KIM, Young-Seok CHOI, Jae-Woo LEE, Woo-Jin SONG, "Partial-Update Normalized Sign LMS Algorithm Employing Sparse Updates" in IEICE TRANSACTIONS on Fundamentals,
vol. E96-A, no. 6, pp. 1482-1487, June 2013, doi: 10.1587/transfun.E96.A.1482.
Abstract: This paper provides a novel normalized sign least-mean square (NSLMS) algorithm which updates only a part of the filter coefficients and simultaneously performs sparse updates with the goal of reducing computational complexity. A combination of the partial-update scheme and the set-membership framework is incorporated into the context of L∞-norm adaptive filtering, thus yielding computational efficiency. For the stabilized convergence, we formulate a robust update recursion by imposing an upper bound of a step size. Furthermore, we analyzed a mean-square stability of the proposed algorithm for white input signals. Experimental results show that the proposed low-complexity NSLMS algorithm has similar convergence performance with greatly reduced computational complexity compared to the partial-update NSLMS, and is comparable to the set-membership partial-update NLMS.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E96.A.1482/_p
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@ARTICLE{e96-a_6_1482,
author={Seong-Eun KIM, Young-Seok CHOI, Jae-Woo LEE, Woo-Jin SONG, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Partial-Update Normalized Sign LMS Algorithm Employing Sparse Updates},
year={2013},
volume={E96-A},
number={6},
pages={1482-1487},
abstract={This paper provides a novel normalized sign least-mean square (NSLMS) algorithm which updates only a part of the filter coefficients and simultaneously performs sparse updates with the goal of reducing computational complexity. A combination of the partial-update scheme and the set-membership framework is incorporated into the context of L∞-norm adaptive filtering, thus yielding computational efficiency. For the stabilized convergence, we formulate a robust update recursion by imposing an upper bound of a step size. Furthermore, we analyzed a mean-square stability of the proposed algorithm for white input signals. Experimental results show that the proposed low-complexity NSLMS algorithm has similar convergence performance with greatly reduced computational complexity compared to the partial-update NSLMS, and is comparable to the set-membership partial-update NLMS.},
keywords={},
doi={10.1587/transfun.E96.A.1482},
ISSN={1745-1337},
month={June},}
Copy
TY - JOUR
TI - Partial-Update Normalized Sign LMS Algorithm Employing Sparse Updates
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1482
EP - 1487
AU - Seong-Eun KIM
AU - Young-Seok CHOI
AU - Jae-Woo LEE
AU - Woo-Jin SONG
PY - 2013
DO - 10.1587/transfun.E96.A.1482
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E96-A
IS - 6
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - June 2013
AB - This paper provides a novel normalized sign least-mean square (NSLMS) algorithm which updates only a part of the filter coefficients and simultaneously performs sparse updates with the goal of reducing computational complexity. A combination of the partial-update scheme and the set-membership framework is incorporated into the context of L∞-norm adaptive filtering, thus yielding computational efficiency. For the stabilized convergence, we formulate a robust update recursion by imposing an upper bound of a step size. Furthermore, we analyzed a mean-square stability of the proposed algorithm for white input signals. Experimental results show that the proposed low-complexity NSLMS algorithm has similar convergence performance with greatly reduced computational complexity compared to the partial-update NSLMS, and is comparable to the set-membership partial-update NLMS.
ER -
English
