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

Tomer Raviv*, Asaf Schwartz*, Yair Be’ery

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

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, and 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 0.75 dB over the CVA in the waterfall region for multiple code lengths, adding negligible computational complexity compared to the circular Viterbi algorithm in high signal-to-noise ratios (SNRs).

Original languageEnglish
Article number93
Pages (from-to)1-13
Number of pages13
JournalEntropy
Volume23
Issue number1
DOIs
StatePublished - Jan 2021

Keywords

  • Deep learning
  • Ensembles
  • Error correcting codes
  • Tail-biting convolutional codes
  • Viterbi, machine learning

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