Deep Ensemble of Weighted Viterbi Decoders for Tail-Biting Convolutional Codes

TL;DR

This paper introduces a machine learning ensemble method for decoding tail-biting convolutional codes, significantly improving error correction performance with minimal additional computational cost, especially in high SNR regimes.

Contribution

It proposes a novel ensemble of weighted Viterbi decoders with a gating mechanism, tailored for short tail-biting codes, enhancing decoding accuracy over the state-of-the-art.

Findings

Achieves up to 0.75dB FER improvement in the waterfall region.

Maintains negligible additional computational complexity.

Effective for multiple code lengths in LTE standards.

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, 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.