The search functionality is under construction.

The search functionality is under construction.

The least mean squares (LMS) algorithm has been widely used for adaptive filtering because of easily implementing at a computational complexity of *O*(2*N*) where *N* is the number of taps. The drawback of the LMS algorithm is that its performance is sensitive to the scaling of the input. The normalized LMS (NLMS) algorithm solves this problem on the LMS algorithm by normalizing with the sliding-window power of the input; however, this normalization increases the computational cost to *O*(3*N*) per iteration. In this work, we derive a new formula to strictly perform the NLMS algorithm at a computational complexity of *O*(2*N*), that is referred to as the C-NLMS algorithm. The derivation of the C-NLMS algorithm uses the H_{∞} framework presented previously by one of the authors for creating a unified view of adaptive filtering algorithms. The validity of the C-NLMS algorithm is verified using simulations.

- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E102-A No.11 pp.1545-1549

- Publication Date
- 2019/11/01

- Publicized

- Online ISSN
- 1745-1337

- DOI
- 10.1587/transfun.E102.A.1545

- Type of Manuscript
- LETTER

- Category
- Digital Signal Processing

Kiyoshi NISHIYAMA

Iwate University

Masahiro SUNOHARA

Rion Co., Ltd.

Nobuhiko HIRUMA

Rion Co., Ltd.

The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.

Copy

Kiyoshi NISHIYAMA, Masahiro SUNOHARA, Nobuhiko HIRUMA, "A New Formula to Compute the NLMS Algorithm at a Computational Complexity of O(2N)" in IEICE TRANSACTIONS on Fundamentals,
vol. E102-A, no. 11, pp. 1545-1549, November 2019, doi: 10.1587/transfun.E102.A.1545.

Abstract: The least mean squares (LMS) algorithm has been widely used for adaptive filtering because of easily implementing at a computational complexity of *O*(2*N*) where *N* is the number of taps. The drawback of the LMS algorithm is that its performance is sensitive to the scaling of the input. The normalized LMS (NLMS) algorithm solves this problem on the LMS algorithm by normalizing with the sliding-window power of the input; however, this normalization increases the computational cost to *O*(3*N*) per iteration. In this work, we derive a new formula to strictly perform the NLMS algorithm at a computational complexity of *O*(2*N*), that is referred to as the C-NLMS algorithm. The derivation of the C-NLMS algorithm uses the H_{∞} framework presented previously by one of the authors for creating a unified view of adaptive filtering algorithms. The validity of the C-NLMS algorithm is verified using simulations.

URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E102.A.1545/_p

Copy

@ARTICLE{e102-a_11_1545,

author={Kiyoshi NISHIYAMA, Masahiro SUNOHARA, Nobuhiko HIRUMA, },

journal={IEICE TRANSACTIONS on Fundamentals},

title={A New Formula to Compute the NLMS Algorithm at a Computational Complexity of O(2N)},

year={2019},

volume={E102-A},

number={11},

pages={1545-1549},

abstract={The least mean squares (LMS) algorithm has been widely used for adaptive filtering because of easily implementing at a computational complexity of *O*(2*N*) where *N* is the number of taps. The drawback of the LMS algorithm is that its performance is sensitive to the scaling of the input. The normalized LMS (NLMS) algorithm solves this problem on the LMS algorithm by normalizing with the sliding-window power of the input; however, this normalization increases the computational cost to *O*(3*N*) per iteration. In this work, we derive a new formula to strictly perform the NLMS algorithm at a computational complexity of *O*(2*N*), that is referred to as the C-NLMS algorithm. The derivation of the C-NLMS algorithm uses the H_{∞} framework presented previously by one of the authors for creating a unified view of adaptive filtering algorithms. The validity of the C-NLMS algorithm is verified using simulations.},

keywords={},

doi={10.1587/transfun.E102.A.1545},

ISSN={1745-1337},

month={November},}

Copy

TY - JOUR

TI - A New Formula to Compute the NLMS Algorithm at a Computational Complexity of O(2N)

T2 - IEICE TRANSACTIONS on Fundamentals

SP - 1545

EP - 1549

AU - Kiyoshi NISHIYAMA

AU - Masahiro SUNOHARA

AU - Nobuhiko HIRUMA

PY - 2019

DO - 10.1587/transfun.E102.A.1545

JO - IEICE TRANSACTIONS on Fundamentals

SN - 1745-1337

VL - E102-A

IS - 11

JA - IEICE TRANSACTIONS on Fundamentals

Y1 - November 2019

AB - The least mean squares (LMS) algorithm has been widely used for adaptive filtering because of easily implementing at a computational complexity of *O*(2*N*) where *N* is the number of taps. The drawback of the LMS algorithm is that its performance is sensitive to the scaling of the input. The normalized LMS (NLMS) algorithm solves this problem on the LMS algorithm by normalizing with the sliding-window power of the input; however, this normalization increases the computational cost to *O*(3*N*) per iteration. In this work, we derive a new formula to strictly perform the NLMS algorithm at a computational complexity of *O*(2*N*), that is referred to as the C-NLMS algorithm. The derivation of the C-NLMS algorithm uses the H_{∞} framework presented previously by one of the authors for creating a unified view of adaptive filtering algorithms. The validity of the C-NLMS algorithm is verified using simulations.

ER -