A new family of gradient-based adaptive filtering algorithms with variable step size

Author

Du, Zhimin ; Wan, Peng ; Pei, Tingrui ; Wu, Weiling

Author_Institution

Sch. of Inf. Eng., Beijing Univ. of Posts & Telecommun., China

Volume

1

fYear

2001

fDate

2001

Firstpage

151

Abstract

Under a uniform framework, this paper develops a new family of adaptive filtering algorithms, where the wellknown normalized LMS algorithm and normalized constant modulus algorithm (CMA) are included. They all update the weight according to the gradient descent method, but this time a variable and relatively optimal step size is used instead of a constant one. Some application examples are also given to show their efficiencies

Keywords

adaptive filters; filtering theory; gradient methods; least mean squares methods; adaptive filtering algorithms; constant modulus algorithm; cost ftinctions; gradient descent method; gradient-based adaptive filtering algorithms; normalized CMA; normalized LMS algorithm; optimal step size; recursive least squares; steady-state performance; variable step size; Adaptive filters; Computational complexity; Convergence; Cost function; Equations; Filtering algorithms; Least squares approximation; Least squares methods; Resonance light scattering; Stochastic processes;

fLanguage

English

Publisher

ieee

Conference_Titel

Global Telecommunications Conference, 2001. GLOBECOM '01. IEEE

Conference_Location

San Antonio, TX

Print_ISBN

0-7803-7206-9

Type

conf

DOI

10.1109/GLOCOM.2001.965097

Filename

965097