Book ChapterDOI
A weighted-output symbol-by-symbol decoding algorithm of binary convolutional codes
J. C. Belfiore
- pp 154-158
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TLDR
A weighted-output symbol-by-symbol soft-decision decoding algorithm for convolutional codes is described, which relies on Bayesian estimation and shows that the latter is a special case of the former.Abstract:
A weighted-output symbol-by-symbol soft-decision decoding algorithm for convolutional codes is described. Its main intended use concerns concatenation schemes. If a convolutional code is used as inner code, it makes soft-decision decoding of the outer code possible, which improves the overall error rate. This algorithm relies on Bayesian estimation. Its comparison with Battail algorithm using cross-entropy minimisation shows that the latter is a special case of the former.read more
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TL;DR: Jaynes's principle of maximum entropy and Kullbacks principle of minimum cross-entropy (minimum directed divergence) are shown to be uniquely correct methods for inductive inference when new information is given in the form of expected values.
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TL;DR: Two specific problems are discussed: the use of previous decisions, which leads to a weighted generalization of feedback decoding, and the extension of replication decoding to nonsystematic codes.
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TL;DR: A means for modifying Viterbi decoding of convolutional codes in order to obtain a reliability estimate of each decoding decision enables soft decoding of the outer code in a concatenated system and leads to a significant improvement of the overall performance.