Convergence Performance Analyses of Fast Data-Reusing Normalized Least Mean Squared Algorithm

Heng-Chou CHEN; Oscal T.-C. CHEN

doi:10.1093/ietfec/e90-a.1.249

Convergence Performance Analyses of Fast Data-Reusing Normalized Least Mean Squared Algorithm

Heng-Chou CHEN, Oscal T.-C. CHEN

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As hardware platforms increase in computation capability, multiple filter coefficient adaptations between two neighboring input signals can be realized to achieve a high convergence rate. This study explores the data-reusing scheme based on the Normalized Least Mean Squared (NLMS) algorithm. Moreover, the impacts of the reusing times and input signal vector's variance to the convergence rate and misadjustment of an adaptive filter are theoretically derived and analyzed. A large number of reusing times was found to raise the convergence rate but also increase misadjustament, while a high variance of an input vector was found to lower misadjustment and expedite the convergence rate. To reduce computational complexity of the data-reusing scheme, this study develops the Fast Data-Reusing NLMS (FDRNLMS) algorithm. The proposed FDRNLMS requires minimal computation for the adaptation scaling factor, and only requires two more multiplication operations than NLMS in calculating each filter output. Additionally, the computation complexity of this FDRNLMS is independent of the number of reusing times. Therefore, the FDRNLMS proposed herein is superior to the NLMS and Least Mean Squared (LMS) algorithms using conventional data-reusing schemes which have computation complexity proportional to the number of reusing times.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E90-A No.1 pp.249-256

Publication Date: 2007/01/01

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Online ISSN: 1745-1337

DOI: 10.1093/ietfec/e90-a.1.249

Type of Manuscript: PAPER

Category: Digital Signal Processing

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Heng-Chou CHEN, Oscal T.-C. CHEN, "Convergence Performance Analyses of Fast Data-Reusing Normalized Least Mean Squared Algorithm" in IEICE TRANSACTIONS on Fundamentals, vol. E90-A, no. 1, pp. 249-256, January 2007, doi: 10.1093/ietfec/e90-a.1.249.
Abstract: As hardware platforms increase in computation capability, multiple filter coefficient adaptations between two neighboring input signals can be realized to achieve a high convergence rate. This study explores the data-reusing scheme based on the Normalized Least Mean Squared (NLMS) algorithm. Moreover, the impacts of the reusing times and input signal vector's variance to the convergence rate and misadjustment of an adaptive filter are theoretically derived and analyzed. A large number of reusing times was found to raise the convergence rate but also increase misadjustament, while a high variance of an input vector was found to lower misadjustment and expedite the convergence rate. To reduce computational complexity of the data-reusing scheme, this study develops the Fast Data-Reusing NLMS (FDRNLMS) algorithm. The proposed FDRNLMS requires minimal computation for the adaptation scaling factor, and only requires two more multiplication operations than NLMS in calculating each filter output. Additionally, the computation complexity of this FDRNLMS is independent of the number of reusing times. Therefore, the FDRNLMS proposed herein is superior to the NLMS and Least Mean Squared (LMS) algorithms using conventional data-reusing schemes which have computation complexity proportional to the number of reusing times.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1093/ietfec/e90-a.1.249/_p

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@ARTICLE{e90-a_1_249,
author={Heng-Chou CHEN, Oscal T.-C. CHEN, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Convergence Performance Analyses of Fast Data-Reusing Normalized Least Mean Squared Algorithm},
year={2007},
volume={E90-A},
number={1},
pages={249-256},
abstract={As hardware platforms increase in computation capability, multiple filter coefficient adaptations between two neighboring input signals can be realized to achieve a high convergence rate. This study explores the data-reusing scheme based on the Normalized Least Mean Squared (NLMS) algorithm. Moreover, the impacts of the reusing times and input signal vector's variance to the convergence rate and misadjustment of an adaptive filter are theoretically derived and analyzed. A large number of reusing times was found to raise the convergence rate but also increase misadjustament, while a high variance of an input vector was found to lower misadjustment and expedite the convergence rate. To reduce computational complexity of the data-reusing scheme, this study develops the Fast Data-Reusing NLMS (FDRNLMS) algorithm. The proposed FDRNLMS requires minimal computation for the adaptation scaling factor, and only requires two more multiplication operations than NLMS in calculating each filter output. Additionally, the computation complexity of this FDRNLMS is independent of the number of reusing times. Therefore, the FDRNLMS proposed herein is superior to the NLMS and Least Mean Squared (LMS) algorithms using conventional data-reusing schemes which have computation complexity proportional to the number of reusing times.},
keywords={},
doi={10.1093/ietfec/e90-a.1.249},
ISSN={1745-1337},
month={January},}

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TY - JOUR
TI - Convergence Performance Analyses of Fast Data-Reusing Normalized Least Mean Squared Algorithm
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 249
EP - 256
AU - Heng-Chou CHEN
AU - Oscal T.-C. CHEN
PY - 2007
DO - 10.1093/ietfec/e90-a.1.249
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E90-A
IS - 1
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - January 2007
AB - As hardware platforms increase in computation capability, multiple filter coefficient adaptations between two neighboring input signals can be realized to achieve a high convergence rate. This study explores the data-reusing scheme based on the Normalized Least Mean Squared (NLMS) algorithm. Moreover, the impacts of the reusing times and input signal vector's variance to the convergence rate and misadjustment of an adaptive filter are theoretically derived and analyzed. A large number of reusing times was found to raise the convergence rate but also increase misadjustament, while a high variance of an input vector was found to lower misadjustment and expedite the convergence rate. To reduce computational complexity of the data-reusing scheme, this study develops the Fast Data-Reusing NLMS (FDRNLMS) algorithm. The proposed FDRNLMS requires minimal computation for the adaptation scaling factor, and only requires two more multiplication operations than NLMS in calculating each filter output. Additionally, the computation complexity of this FDRNLMS is independent of the number of reusing times. Therefore, the FDRNLMS proposed herein is superior to the NLMS and Least Mean Squared (LMS) algorithms using conventional data-reusing schemes which have computation complexity proportional to the number of reusing times.
ER -