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).

中文翻译：

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用于咬尾卷积码的加权维特比解码器的深度集成

咬尾卷积码扩展了经典的零终止卷积码：两种编码方案都强制开始和结束状态相等，但在咬尾下，每个状态都是有效的终止。本文提出了一种机器学习方法来改进咬尾码的最新解码，重点关注 LTE 标准中广泛采用的短长度机制。该标准还包括一个 CRC 代码。首先，我们对循环维特比算法进行参数化，这是一种利用底层网格的循环特性的基线解码器。一个集成组合了多个这样的加权解码器，每个解码器专门解码来自通道词分布的特定区域的词。一个区域对应一个终止状态的子集；整体覆盖了整个国家空间。不可学习的门控满足两个目标：过滤容易解码的单词并减轻执行多个加权解码器的开销。CRC 标准用于仅选择专家子集用于解码目的。对于多个码长，我们的方法在瀑布区域的 CVA 上实现了高达 0.75 dB 的 FER 改进，与高信噪比 (SNR) 中的圆形 Viterbi 算法相比，增加的计算复杂度可以忽略不计。