Cart (Loading....) | Create Account
Close category search window
 

Divide and Concur and Difference-Map BP Decoders for LDPC Codes

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)
Yedidia, Jonathan S. ; Mitsubishi Electr. Res. Labs., Cambridge, MA, USA ; Yige Wang ; Draper, S.C.

The “Divide and Concur” (DC) algorithm introduced by Gravel and Elser can be considered a competitor to the belief propagation (BP) algorithm, in that both algorithms can be applied to a wide variety of constraint satisfaction, optimization, and inference problems. We show that DC can be interpreted as a message-passing algorithm on a “normal” factor graph. The “difference-map” dynamics of the DC algorithm enables it to avoid “traps” which may be related to the “trapping sets” or “pseudo-codewords” that plague BP decoders of low-density parity check (LDPC) codes in the error-floor regime. We investigate two decoders for LDPC codes based on these ideas. The first decoder is based directly on DC, while the second decoder borrows the important “difference-map” concept from the DC algorithm and translates it into a BP-like decoder. We show that this “difference-map belief propagation” (DMBP) decoder has dramatically improved error-floor performance compared to standard BP decoders, while maintaining a similar computational complexity. We present simulation results for LDPC codes comparing DC and DMBP decoders with other decoders based on sum-product BP, linear programming, and mixed-integer linear programming. We also describe the close relation of the DMBP decoder to reweighted min-sum algorithms, including those recently proposed by Ruozzi and Tatikonda.

Published in:

Information Theory, IEEE Transactions on  (Volume:57 ,  Issue: 2 )

Date of Publication:

Feb. 2011

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.