Abstract

A method is described for constructing long error-correcting codes from one or more shorter error-correcting codes, referred to as subcodes, and a bipartite graph. A graph is shown which specifies carefully chosen subsets of the digits of the new codes that must be codewords in one of the shorter subcodes. Lower bounds to the rate and the minimum distance of the new code are derived in terms of the parameters of the graph and the subeodes. Both the encoders and decoders proposed are shown to take advantage of the code's explicit decomposition into subcodes to decompose and simplify the associated computational processes. Bounds on the performance of two specific decoding algorithms are established, and the asymptotic growth of the complexity of decoding for two types of codes and decoders is analyzed. The proposed decoders are able to make effective use of probabilistic information supplied by the channel receiver, e.g., reliability information, without greatly increasing the number of computations required. It is shown that choosing a transmission order for the digits that is appropriate for the graph and the subcodes can give the code excellent burst-error correction abilities. The construction principles

Keywords

Factor graphLow-density parity-check codeBipartite graphDecoding methodsComputer scienceAlgorithmTanner graphEncoderCode rateBlock codeProbabilistic logicTheoretical computer scienceConcatenated error correction codeMathematicsGraph

Affiliated Institutions

Related Publications

Codes and Decoding on General Graphs

Iterative decoding techniques have become a viable alternative for constructing high performance coding systems. In particular, the recent success of turbo codes indicates that ...

1996 908 citations

Publication Info

Year
1981
Type
article
Volume
27
Issue
5
Pages
533-547
Citations
3080
Access
Closed

Social Impact

Social media, news, blog, policy document mentions

Citation Metrics

3080
OpenAlex
234
Influential
2226
CrossRef

Cite This

R. Michael Tanner (1981). A recursive approach to low complexity codes. IEEE Transactions on Information Theory , 27 (5) , 533-547. https://doi.org/10.1109/tit.1981.1056404

Identifiers

DOI
10.1109/tit.1981.1056404

Data Quality

Data completeness: 77%