By Topic

The adaptive RBFNN equalizer for nonlinear time-varying UMTS channel

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

4 Author(s)
Assaf, R. ; Commun. Dept., Lebanese Univ., Hadath, Lebanon ; Elassad, S. ; Harkouss, Y. ; Zoaeter, M.

The paper presents an adaptive RBFNNE (radial basis function neural network equalizer) of nonlinear time-varying UMTS channel; the architecture of the RBFNNE implements the Bayesian decision function. Centers of hidden layer neurons, equal to the channel states, are determined by an unsupervised classification algorithm based on the rival penalized competitive algorithm. To determine the other parameters (spreads of hidden layer neurons and connections weights), the gradient descent algorithm applying the on-line and offline training modes is used. The mobile communication channel UMTS, is generally modeled by a tapped delay line (TDL) model, where the coefficients implement the delay profile and the Doppler effect of the doubly-selective channel. Furthermore, a nonlinear distortion is added to the transmitted symbols; good performance results are obtained as compared to the classical equalizers i.e. minimum mean-squared error equalizer (MMSE) and the decision feedback equalizer (DFE) are obtained for the case of nonlinear time-varying UMTS channel.

Published in:

Advances in Computational Tools for Engineering Applications, 2009. ACTEA '09. International Conference on

Date of Conference:

15-17 July 2009