论文标题
加权Viterbi解码器的深度合奏，用于尾巴卷积代码
Deep Ensemble of Weighted Viterbi Decoders for Tail-Biting Convolutional Codes
论文作者
论文摘要
尾巴卷积代码扩展了经典的零终端卷积代码：两个编码方案都迫使起始状态和最终状态的平等性，但是在尾巴刺痛下，每个状态都是有效的终止。本文提出了一种机器学习方法，以改善尾巴尺寸的最新解码，重点是LTE标准中广泛使用的短长度制度。该标准还包括CRC代码。 首先，我们将圆形Viterbi算法进行参数化，这是一种基线解码器，可利用下面的格子的圆形性质。合奏结合了多个这样的加权解码器，每个解码器都专门研究来自通道单词分布的特定区域的单词。一个区域对应于终止状态的一部分；合奏覆盖了整个状态空间。一个非可行性的门控满足了两个目标：它可以过滤易于解码单词，并减轻执行多个加权解码器的开销。 CRC标准被用来仅选择一部分专家来解码目的。我们的方法可在瀑布区域的CVA上获得多达0.75dB的速度，以达到多个代码长度，与高SNR中的圆形Viterbi算法相比，可以增加可忽略的计算复杂性。
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.
