By Topic

Minimax robust coding for channels with uncertainty statistics

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)

The problem of minimax robust coding for classes of channels with uncertainty in their statistical description is addressed. Specific consideration is given to: 1) discrete memoryless channels with uncertainty in the probability transition matrices; 2) discrete-time stationary Gaussian channels with spectral uncertainty; and to uncertainty with classes determined by 2-alternating Choquet capacities. Both block codes and convolutional codes are considered. A robust maximum-likelihood decoding rule is derived; the rule guarantees that, for all channels in the uncertainty class and all rates smaller than a critical rate, the average probability of decoding error for the ensemble of random block codes and the ensemble of random time-varying convolutional codes converges to zero exponentially with increasing block length or constraint length, respectively. The channel capacity and cut-off rate of the class are then evaluated.

Published in:

Information Theory, IEEE Transactions on  (Volume:31 ,  Issue: 6 )