Conditionally Cycle-Free Generalized Tanner Graphs: Theory and Application to High-Rate Serially Concatenated Codes

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dc.creatorHalford, Thomas R.
dc.creatorChugg, Keith M.
dc.date2006-11-20
dc.date2007-03-26
dc.date.accessioned2026-07-07T08:16:49Z
dc.date.available2026-07-07T08:16:49Z
dc.descriptionGeneralized Tanner graphs have been implicitly studied by a number of authors under the rubric of generalized parity-check matrices. This work considers the conditioning of binary hidden variables in such models in order to break all cycles and thus derive optimal soft-in soft-out (SISO) decoding algorithms. Conditionally cycle-free generalized Tanner graphs are shown to imply optimal SISO decoding algorithms for the first order Reed-Muller codes and their duals - the extended Hamming codes - which are substantially less complex than conventional bit-level trellis decoding. The study of low-complexity optimal SISO decoding algorithms for the family of extended Hamming codes is practically motivated. Specifically, it is shown that exended Hamming codes offer an attractive alternative to high-rate convolutional codes in terms of both performance and complexity for use in very high-rate, very low-floor, serially concatenated coding schemes.
dc.description5 pages, minor revisions, to be presented at the 2007 IEEE Int'l Symposium on Information Theory
dc.identifierhttps://arxiv.org/abs/cs/0611097
dc.identifierhttp://arxiv.org/abs/cs/0611097
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/133919
dc.subjectInformation Theory
dc.titleConditionally Cycle-Free Generalized Tanner Graphs: Theory and Application to High-Rate Serially Concatenated Codes
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