Decoding error-correcting codes via linear programming
Decoding error-correcting codes via linear programming
The extraction and complexity limits of graphical models for linear codes
The extraction and complexity limits of graphical models for linear codes
The geometry of turbo-decoding dynamics
IEEE Transactions on Information Theory
Using linear programming to Decode Binary linear codes
IEEE Transactions on Information Theory
Iterative Soft-Input Soft-Output Decoding of Reed–Solomon Codes by Adapting the Parity-Check Matrix
IEEE Transactions on Information Theory
Pseudocodewords of Tanner Graphs
IEEE Transactions on Information Theory
Iterative decoding on multiple Tanner graphs using random edge local complementation
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 2
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An iterative algorithm for soft-input soft-output (SISO) decoding of classical algebraic cyclic block codes is presented below. Inspired by other approaches for high performance belief propagation (BP) decoding, this algorithm requires up to 10 times less computational complexity than other methods that achieve similar performance. By utilizing multiple BP decoders, and using random permutation taken from the permutation group of the code, this algorithm reaches near maximum likelihood performance. A computational complexity comparison of the proposed algorithm versus other methods is presented as well. This includes complexity versus performance analysis, allowing one to trade between the former and the latter, according to ones needs.