Low-complexity ML decoding for convolutional tail-biting codes

Hung Ta Pai*, Yungh Siang Han, Ting Yi Wu, Po-Ning Chen, Shin Lin Shieh

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

25 Scopus citations


Recently, a maximum-likelihood (ML) decoding algorithm with two phases has been proposed for convolutional tailbiting codes [1]. The first phase applies the Viterbi algorithm to obtain the trellis information, and then the second phase employs the algorithm A* to find the ML solution. In this work, we improve the complexity of the algorithm A* by using a new evaluation function. Simulations showed that the improved Av algorithm has over 5 times less average decoding complexity in the second phase when Eb/N0≥ 4 dB.

Original languageEnglish
Pages (from-to)883-885
Number of pages3
JournalIEEE Communications Letters
Issue number12
StatePublished - 1 Dec 2008


  • Algorithm A*
  • Maximum-likelihood
  • Tailbiting codes
  • Viterbi algorithm


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