By Topic

Analysis of the multiple-error and block least-mean-square adaptive algorithms

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
Douglas, S.C. ; Dept. of Electr. Eng., Utah Univ., Salt Lake City, UT, USA

In some block-based and frequency-domain filtering tasks and in multichannel filtering applications, a multiple-error LMS adaptive algorithm, given by Wk+1=Wk+μXk(D k-XkTWk), is employed, In this paper, we examine the mean-square performance of the multiple-error LMS adaptive algorithm for correlated Gaussian input data channels and arbitrary i.i.d. input data channels. We provide a new mean-square analysis of this algorithm that accounts for the correlations between successive data vectors in the data matrix Xk. Using our analysis, we show that for both correlated and i.i.d. input data channels, the multiple-error LMS algorithm performs uniformly worse than the single-channel LMS algorithm for a given amount of data consumed. We also derive simple step size bounds to guarantee mean-square convergence of the multiple-error and block LMS adaptive algorithms for our correlated data model. Simulations of both the block LMS adaptive algorithm and the multichannel filtered-X LMS adaptive algorithm corroborate our theoretical results

Published in:

Circuits and Systems II: Analog and Digital Signal Processing, IEEE Transactions on  (Volume:42 ,  Issue: 2 )