Constrained iterative decoding: Performance and convergence analysis

Jun Heo, Keith M. Chugg

Research output: Contribution to journalConference articlepeer-review

4 Citations (Scopus)


We introduce a modification to the standard iterative decoding (message passing) algorithm that yields improved performance at the cost of higher complexity. This modification is to run multiple iterative decoders, each with a different constraint on a system variable (e.g., input value, state value, etc.). This Constrained Iterative Decoding (CID) implements optimal MAP decoding for systems represented by single-cycle graphs (e.g., tail-biting convolutional codes). For more complex graphical models, the CID is suboptimal, but outperforms the standard decoding algorithm because it negates the effects of some cycles in the model. In this paper, we show that the CID outperforms the standard ID for a Serially Concatenated Convolution Code (SCCC) system and Low-Density-Parity-Check (LDPC) code system, especially when the interleaver size is small. Density evolution analysis is used to show how CID improves the convergence relative to that of standard ID by showing that the threshold of the CID is lower than that of the standard ID.

Original languageEnglish
Pages (from-to)275-279
Number of pages5
JournalConference Record of the Asilomar Conference on Signals, Systems and Computers
Publication statusPublished - 2001
Externally publishedYes
Event35th Asilomar Conference on Signals, Systems and Computers - Pacific Grove, CA, United States
Duration: 2001 Nov 42001 Nov 7

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications


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