By Topic

Adaptive Equalization of Nonlinear Time Varying-Channels using Radial Basis Network

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

3 Author(s)
Assaf, R. ; Ecole Polytechnique, Univ. de Nantes ; El Assad, S. ; Harkouss, Y.

The paper investigates adaptive equalization of nonlinear time varying digital communication channel. An architecture of equalization was proposed based on the Bayesian theory (R. Assaf et al., 2005) where an implementation by radial basis function neural network (RBFNN) was accomplished. We treated the equalization of binary transmission signal through dispersive nonlinear time varying channel. The hybrid training algorithm is used. For the supervised part, it uses the sequential LMS algorithm which has a good convergence over batch LMS algorithm. For the unsupervised part, the rival penalized competitive learning method is used, with the LBG algorithm for the initial values. The performance of the equalizer is compared with the Bayesian equalizer which has the optimal parameters

Published in:

Information and Communication Technologies, 2006. ICTTA '06. 2nd  (Volume:1 )

Date of Conference:

0-0 0