Analytical development of the MMAXNLMS algorithm

Author

Haddad, M.I. ; Mayyas, K.A. ; Khasawneh, M.A.

Author_Institution

Dept. of Electr. Eng., Jordan Univ. of Sci. & Technol., Irbid, Jordan

Volume

4

fYear

1999

fDate

15-19 Mar 1999

Firstpage

1853

Abstract

In this paper an adaptive algorithm with reduced complexity is analysed for the white Gaussian input case. The new analysis is extended for the proposed case where updating includes more than one component of the weight vector. The new algorithm, which updates the weights corresponding to the element sizes of the data vector with the largest magnitude, is compared with the case where the updated weights are chosen randomly according to a uniform density function. Analysis is performed for both cases and the results are verified via computer simulations

Keywords

Gaussian noise; adaptive signal processing; computational complexity; least mean squares methods; minimax techniques; white noise; MMAXNLMS algorithm; adaptive algorithm; analytical development; computer simulation; data vector; uniform density function; weight vector; white Gaussian noise; Adaptive algorithm; Adaptive filters; Algorithm design and analysis; Analytical models; Computational complexity; Computational modeling; Computer simulation; Density functional theory; Performance analysis; Random processes;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1999. Proceedings., 1999 IEEE International Conference on

Conference_Location

Phoenix, AZ

ISSN

1520-6149

Print_ISBN

0-7803-5041-3

Type

conf

DOI

10.1109/ICASSP.1999.758283

Filename

758283