Deep ensemble of weighted Viterbi decoders for tail-biting convolutional codes

Tomer Raviv, Asaf Schwartz, Yair Be’ery

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Tail-biting convolutional codes extend the classical zero-termination convolutional codes: Both encoding schemes force the equality of start and end states, but under the tail-biting each state is a valid termination. This paper proposes a machine-learning approach to improve the state-of-the-art decoding of tail-biting codes, focusing on the widely employed short length regime as in the LTE standard. This standard also includes a CRC code. First, we parameterize the circular Viterbi algorithm, a baseline decoder that exploits the circular nature of the underlying trellis. An ensemble combines multiple such weighted decoders, each decoder specializes in decoding words from a specific region of the channel words’ distribution. A region corresponds to a subset of termination states; the ensemble covers the entire states space. A non-learnable gating satisfies two goals: it filters easily decoded words and mitigates the overhead of executing multiple weighted decoders. The CRC criterion is employed to choose only a subset of experts for decoding purpose. Our method achieves FER improvement of up to 0.75dB over the CVA in the waterfall region for multiple code lengths, adding negligible computational complexity compared to the circular Viterbi algorithm in high SNRs.

Original languageEnglish
Title of host publication2020 IEEE Information Theory Workshop, ITW 2020
PublisherInstitute of Electrical and Electronics Engineers
ISBN (Electronic)9781728159621
StatePublished - 11 Apr 2021
Externally publishedYes
Event2020 IEEE Information Theory Workshop, ITW 2020 - Virtual, Riva del Garda, Italy
Duration: 11 Apr 202115 Apr 2021

Publication series

Name2020 IEEE Information Theory Workshop, ITW 2020


Conference2020 IEEE Information Theory Workshop, ITW 2020
CityVirtual, Riva del Garda


  • Deep Learning
  • Ensembles
  • Error Correcting Codes
  • Machine Learning
  • Tail-Biting Convolutional Codes
  • Viterbi

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Information Systems
  • Signal Processing
  • Software
  • Theoretical Computer Science


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