Frank R. Kschischang and Brendan J. Frey 1998.
Iterative decoding of compound codes by probability propagation in
graphical models.
IEEE Journal on Selected Areas in Communications 16,
219-230.
We present a unified graphical model framework
for describing compound codes and deriving iterative decoding algorithms.
After reviewing a variety of graphical models
(Markov random fields, Tanner graphs, and Bayesian networks),
we derive a general distributed marginalization algorithm
for functions described by factor graphs.
From this general algorithm,
Pearl's belief propagation algorithm is easily
derived as a special case.
We point out that recently developed iterative
decoding algorithms for
various codes, including ``turbo decoding'' of
parallel-concatenated convolutional codes, may be viewed as
probability propagation in a graphical model of the code.
We focus on Bayesian network descriptions of codes, which
give a natural input/state/output/channel description of
a code and channel, and we indicate how iterative
decoders can be developed for parallel- and serially-concatenated
coding systems, product codes, and low-density parity-check
codes.
Compressed postscript,
uncompressed postscript.
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