By Topic

On the asymptotic input-output weight distributions and thresholds of convolutional and turbo-like encoders

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)

We present a general method for computing the asymptotic input-output weight distribution of convolutional encoders. In some instances, one can derive explicit analytic expressions. In general, though, to determine the growth rate of the input-output weight distribution for a particular normalized input weight κ and output weight ω, a system of polynomial equations has to be solved. This method is then used to determine the asymptotic weight distribution of various concatenated code ensembles and to derive lower bounds on the thresholds of these ensembles under maximum-likelihood (ML) decoding.

Published in:

Information Theory, IEEE Transactions on  (Volume:48 ,  Issue: 12 )