Alternate document:   Details   The Distribution of Cycle Lengths in Graphical Models for Turbo Decoding (99) Xianping Ge, David Eppstein, Padhraic Smyth

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The Distribution of Cycle Lengths in Graphical Models for Iterative Decoding (1999)  (Make Corrections)  (6 citations)
Xian-ping Ge, David Eppstein, and Padhraic Smyth



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Abstract: This paper analyzes the distribution of cycle lengths in turbo decoding and low-density parity check (LDPC) graphs. The properties of such cycles are of significant interest in the context of iterative decoding algorithms which are based on belief propagation or message passing. We estimate the probability that there exist no simple cycles of length less than or equal to k at a randomly chosen node in a turbo decoding graph using a combination of counting arguments and independence assumptions. ... (Update)

Context of citations to this paper:   More

.... is shown to eliminate very short loops and for larger loops results in only a small systematic decrease in the probability of such loops [7]. For other codes with similar iterative decoding algorithms to turbo codes, the same techniques of analysis can be applied. For...

...in coding networks has demonstrated that a node has a low probability (less than 0. 01) of being in a cycle of length less than or equal to 10 [5]. Furthermore, the CPTs derived for edges with low Gaussian noise oe define very strong correlation between parent child node values....

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BibTeX entry:   (Update)

X. Ge, D. Eppstein, and P. Smyth (1999). The distribution of cycle lengths in graphical models for iterative decoding. Technical Report UCI-ICS 99-10, March 1999. Available at http:// www.ics.uci.edu/~datalab/papers.html. http://citeseer.ist.psu.edu/ge99distribution.html   More

@techreport{ ge99distribution,
  author = "X. Ge and D. Eppstein and P. Smyth",
  title = "The distribution of cycle lengths in graphical models for iterative decoding",
  number = "UCI-ICS 99-10",
  month = mar,
  year = "1999",
  url = "citeseer.ist.psu.edu/ge99distribution.html" }
Citations (may not include all citations):
1543   Probabilistic Reasoning in Intelligent Systems: Networks of .. (context) - Pearl - 1988
559   Near Shannon limit errorcorrecting coding and decoding: Turb.. (context) - Berrou, Glavieux et al. - 1993
442   Optimal decoding of linear codes for minimizing symbol error.. (context) - Bahl, Cocke et al. - 1974
341   Low-Density Parity-Check Codes (context) - Gallager - 1963
115   Graphical Models for Machine Learning and Digital Communicat.. (context) - Frey - 1998
105   Probabilistic independence networks for hidden Markov probab.. - Smyth, Heckerman et al. - 1997
99   The Capacity of Low-Density Parity Check Codes under Message.. - Richardson, Urbanke - 1998
82   Near Shannon Limit Performance of Low Density Parity Check C.. - MacKay, Neal - 1996
57   Iterative Decoding of Compound Codes by Probability Propagat.. - Kschischang, Frey - 1998
49   Belief Propagation (context) - McEliece, MacKay et al. - 1998
44   Correctness of local probability propagation in graphical mo.. - Weiss - 1998
38   Weight Distributions for Turbo Codes Using Random and Nonran.. (context) - Dolinar, Divsalar - 1995
37   Analysis of Low Density Codes and Improved Designs Using Irr.. - Luby, Mitzenmacher et al. - 1998
35   The Turbo-decision Algorithm (context) - McEliece, Rodemich et al. - 1995
8   Interleaver Design Methods for Turbo Codes (context) - Andrews, Heegard et al. - 1998
5   Turbo Coding (context) - Heegard, Wicker - 1998
1   Distance effects in message propagation (context) - Ge, Smyth

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