EXIT Chart Approximations using the Role Model Approach

Computer Science – Information Theory

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

5 pages, 5 figures, to be presented at the IEEE Symposium on Information Theory (ISIT 2010) in Austin, Texas, June 2010

Scientific paper

Extrinsic Information Transfer (EXIT) functions can be measured by statistical methods if the message alphabet size is moderate or if messages are true a-posteriori distributions. We propose an approximation we call mixed information that constitutes a lower bound for the true EXIT function and can be estimated by statistical methods even when the message alphabet is large and histogram-based approaches are impractical, or when messages are not true probability distributions and time-averaging approaches are not applicable. We illustrate this with the hypothetical example of a rank-only message passing decoder for which it is difficult to compute or measure EXIT functions in the conventional way. We show that the role model approach (arXiv:0809.1300) can be used to optimize post-processing for the decoder and that it coincides with Monte Carlo integration in the non-parametric case. It is guaranteed to tend towards the optimal Bayesian post-processing estimator and can be applied in a blind setup with unknown code-symbols to optimize the check-node operation for non-binary Low-Density Parity-Check (LDPC) decoders.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

EXIT Chart Approximations using the Role Model Approach does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with EXIT Chart Approximations using the Role Model Approach, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and EXIT Chart Approximations using the Role Model Approach will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-513823

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.