222 research outputs found
Deep Ensemble of Weighted Viterbi Decoders for Tail-Biting Convolutional Codes
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
- …