Improved random redundant iterative HDPC decoding

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...

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Veröffentlicht in:IEEE transactions on communications Jg. 57; H. 7; S. 1982 - 1985
Hauptverfasser: Dimnik, I., Be'ery, Y.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York, NY IEEE 01.07.2009
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Applied sciences
Back propagation
Belief propagation
Block codes
Coding, codes
Complexity
Computation
Computational complexity
Decoding
Exact sciences and technology
Information, signal and communications theory
Iterative algorithms
Iterative decoding
Iterative methods
Linear programming
Maximum likelihood decoding
message-passing algorithm
Parity check codes
Performance analysis
permutation group
Permutations
Redundant
Signal and communications theory
soft-decision decoding
Telecommunications and information theory
Performance evaluation
Block code
High performance
Cyclic code
Algebraic code
Iterative method
message-passing algorithm
Algorithm
Computational complexity
Iterative decoding
Credal approach
Message passing
Permutation group
Maximum likelihood
soft-decision decoding
Soft decision
Time complexity
ISSN:0090-6778, 1558-0857
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung: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.
Bibliographie:ObjectType-Article-2
SourceType-Scholarly Journals-1
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ISSN:0090-6778
1558-0857
DOI:10.1109/TCOMM.2009.07.070621