Scheduled System Maintenance:
Some services will be unavailable Sunday, March 29th through Monday, March 30th. We apologize for the inconvenience.
By Topic

Fast nonlinear channel equalisation using generalised diagonal recurrent neural networks

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 $31
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

2 Author(s)
Yong-Woon Kim ; Dept. of Electr. Eng., Korea Adv. Inst. of Sci. & Technol., Seoul ; Dong-Jo Park

A generalised diagonal recurrent neural network (GDRNN) for nonlinear channel equalisation is proposed. The hidden nodes of the GDRNN have recurrent weights to capture the dynamic characteristics of the communication channels. The learning algorithm of the proposed GDRNN is derived, based on constrained optimisation. The proposed neural network gives faster learning speed and has better convergence properties than do conventional channel equalisers

Published in:

Electronics Letters  (Volume:34 ,  Issue: 23 )