An improved model for the Normalized LMS algorithm with Gaussian inputs and large number of coefficients

This work presents a new analytical model for the Normalized Least Mean Square (NLMS) algorithm for Gaussian signals and large number of coefficients. These characteristics are of special interest in applications such as echo canceling and active noise control. The new results are compared to previo...

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Bibliographic Details
Main Authors: Costa, Marcio H., Bermudez, Jose C.M
Format: Conference Proceeding
Language:English
Subjects:
Lead
Least squares approximation
Steady-state
Online Access:Request full text
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Description
Summary:This work presents a new analytical model for the Normalized Least Mean Square (NLMS) algorithm for Gaussian signals and large number of coefficients. These characteristics are of special interest in applications such as echo canceling and active noise control. The new results are compared to previous models described in the literature, showing a better agreement with Monte Carlo simulations even for unit step-size (maximum convergence speed). The new results contribute to an ongoing discussion about the relative performances of the NLMS and LMS algorithms. The new model shows that both algorithms have similar performances for a large number of coefficients and properly chosen step-sizes.
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.2002.5745810