• Partial update LMS algorithms

Result: Partial update LMS algorithms

Title:
Partial update LMS algorithms
Authors:
GODAVARTI, Mahesh, HERO, Alfred O
Source:
IEEE transactions on signal processing. 53(7):2382-2399
Publisher Information:
New York, NY: Institute of Electrical and Electronics Engineers, 2005.
Publication Year:
2005
Physical Description:
print, 25 ref
Original Material:
INIST-CNRS
Subject Terms:
Telecommunications, Télécommunications, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Telecommunications et theorie de l'information, Telecommunications and information theory, Théorie de l'information, du signal et des communications, Information, signal and communications theory, Théorie du signal et des communications, Signal and communications theory, Signal, bruit, Signal, noise, Détection, estimation, filtrage, égalisation, prédiction, Detection, estimation, filtering, equalization, prediction, Algorithme, Algorithm, Algoritmo, Complexité calcul, Computational complexity, Complejidad computación, Condition suffisante, Sufficient condition, Condición suficiente, Consommation énergie électrique, Power consumption, Erreur quadratique moyenne, Mean square error, Error medio cuadrático, Etude théorique, Theoretical study, Estudio teórico, Filtre adaptatif, Adaptive filter, Filtro adaptable, Implémentation, Implementation, Implementación, Méthode moindre carré moyen, Least mean squares methods, Méthode séquentielle, Sequential method, Método secuencial, Ordonnancement, Scheduling, Reglamento, Régime permanent, Steady state, Régimen permanente, Signal stationnaire, Stationary signal, Señal estacionaria, Simulation numérique, Numerical simulation, Simulación numérica, Stabilité exponentielle, Exponential stability, Estabilidad exponencial, Taux convergence, Convergence rate, Relación convergencia, max partial update, partial update LMS algorithms, periodic algorithm, random updates, sequential algorithm, set-membership
Document Type:
Academic journal Article
File Description:
text
Language:
English
Author Affiliations:
Ditech Communications, Inc, Mountain View, CA 94043, United States
Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, United States
ISSN:
1053-587X
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=16911700
Rights:
Copyright 2005 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Telecommunications and information theory
Accession Number:
edscal.16911700
Database:
PASCAL Archive

Further Information

Partial updating of LMS filter coefficients is an effective method for reducing computational load and power consumption in adaptive filter implementations. This paper presents an analysis of convergence of the class of Sequential Partial Update LMS algorithms (S-LMS) under various assumptions and shows that divergence can be prevented by scheduling coefficient updates at random, which we call the Stochastic Partial Update LMS algorithm (SPU-LMS). Specifically, under the standard independence assumptions, for wide sense stationary signals, the S-LMS algorithm converges in the mean if the step-size parameter μ is in the convergent range of ordinary LMS. Relaxing the independence assumption, it is shown that S-LMS and LMS algorithms have the same sufficient conditions for exponential stability. However, there exist nonstationary signals for which the existing algorithms, S-LMS included, are unstable and do not converge for any value of μ. On the other hand, under broad conditions, the SPU-LMS algorithm remains stable for nonstationary signals. Expressions for convergence rate and steady-state mean-square error of SPU-LMS are derived. The theoretical results of this paper are validated and compared by simulation through numerical examples.