Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.13/26042
- Title
- Stochastic analysis of turbo decoding
- Author/Creator
-
Fu, Minyue
- Description
- This paper proposes a stochastic framework for dynamic modeling and analysis of turbo decoding. By modeling the input and output signals of a turbo decoder as random processes, we prove that these signals become ergodic when the block size of the code becomes very large. This basic result allows us to easily model and compute the statistics of the signals in a turbo decoder. Using the ergodicity result and the fact that a sum of lognormal distributions is well approximated using a lognormal distribution, we show that the input-output signals in a turbo decoder, when expressed using log-likelihood ratios (LLRs), are well approximated using Gaussian distributions. Combining the two results above, we can model a turbo decoder using two input parameters and two output parameters (corresponding to the means and variances of the input and output signals). Using this model, we are able to reveal the whole dynamics of a decoding process. We have discovered that a typical decoding process is much more intricate than previously known, involving two regions of attraction, several fixed points, and a stable equilibrium manifold at which all decoding trajectories converge. Some applications of the stochastic framework are also discussed, including a fast decoding scheme.
- Relation
- IEEE Transactions On Information Theory Vol. 51, Issue 1, p. 81-100
- Relation
- http://dx.doi.org/10.1109/TIT.2004.839494
- Date
- 2005
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Keyword(s)
-
Gaussian distribution;
block codes;
convergence of numerical methods;
iterative decoding;
log normal distribution;
parity check codes;
turbo codes
- Resource Type
- journal article
- Rights
- Copyright © 2005 IEEE. Reprinted from IEEE Transactions On Information Theory. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Newcastle's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.
- Identifier
- http://hdl.handle.net/1959.13/26042
- Identifier
- ISSN:0018-9448
- Reviewed
- Full Text
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