A Low-Complexity Maximum-Likelihood Decoder for Tail-Biting Convolutional Codes

Yunghsiang S. Han, Ting Yi Wu, Po Ning Chen, Pramod Kumar Varshney

Research output: Contribution to journalArticle

5 Scopus citations

Abstract

Due to the growing interest in applying tail-biting convolutional coding techniques in real-time communication systems, fast decoding of tail-biting convolutional codes has become an important research direction. In this paper, a new maximum-likelihood decoder for tail-biting convolutional codes is proposed. It is named bidirectional priority-first search algorithm (BiPFSA) because priority-first search algorithm has been used both in forward and backward directions during decoding. Simulations involving the antipodal transmission of (2, 1, 6) and (2, 1, 12) tail-biting convolutional codes over additive white Gaussian noise channels shows that BiPFSA not only has the least average decoding complexity among the state-of-the-art decoding algorithms for tail-biting convolutional codes but can also provide a highly stable decoding complexity with respect to growing information length and code constraint length. More strikingly, at high SNR, its average decoding complexity can even approach the ideal benchmark complexity, obtained under a perfect noise-free scenario by any sequential-type decoding. This demonstrates the superiority of BiPFSA in terms of decoding efficiency.

Original languageEnglish (US)
Pages (from-to)1859-1870
Number of pages12
JournalIEEE Transactions on Communications
Volume66
Issue number5
DOIs
StatePublished - May 1 2018

Keywords

  • convolutional codes
  • maximum-likelihood decoding
  • Tail-biting convolutional codes
  • viterbi algorithm

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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