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arXiv 2201.11935cs.ITmath.IT

卷积码序列解码用于同步误差

Sequential Decoding of Convolutional Codes for Synchronization Errors

Anisha Banerjee, Andreas Lenz, Antonia Wachter-Zeh

AI总结:

本文提出一种适用于易发生插入、删除和替换误差信道的卷积码序列解码器,通过引入漂移状态扩展码树,并改进Fano度量,降低解码复杂度,但牺牲少量误码率。

摘要:

序列解码常用于替代信道,是比维特比解码次优的选择,具有显著降低的内存成本。本文描述并分析了一种适用于易发生插入、删除和替换误差信道的卷积码序列解码器。我们的解码器通过引入新的信道状态变量,称为漂移状态,扩展码树。在该树上推导出适合序列解码的度量,推广了原始Fano度量。解码器还被扩展以同时解码多个接收到的序列,这些序列源自单个发送序列。在低噪声环境中,我们的解码方法将解码复杂度降低几个数量级,尽管误码率略有增加。还提出了一种确定计算截止率的分析方法。此分析通过误码率和计算复杂度的数值评估得到支持,并与最优维特比解码进行比较。

英文摘要:

Sequential decoding, commonly applied to substitution channels, is a sub-optimal alternative to Viterbi decoding with significantly reduced memory costs. In this work, a sequential decoder for convolutional codes over channels that are prone to insertion, deletion, and substitution errors, is described and analyzed. Our decoder expands the code trellis by a new channel-state variable, called drift state, as proposed by Davey and MacKay. A suitable decoding metric on that trellis for sequential decoding is derived, generalizing the original Fano metric. The decoder is also extended to facilitate the simultaneous decoding of multiple received sequences that arise from a single transmitted sequence. Under low-noise environments, our decoding approach reduces the decoding complexity by a couple orders of magnitude in comparison to Viterbi's algorithm, albeit at slightly higher bit error rates. An analytical method to determine the computational cutoff rate is also suggested. This analysis is supported with numerical evaluations of bit error rates and computational complexity, which are compared with respect to optimal Viterbi decoding.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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