基于统计学习的咬尾卷积码BMST译码
Statistical Learning Aided Decoding of BMST of Tail-Biting Convolutional Code
摘要：
研究了咬尾卷积码的分组马尔可夫叠加传输。提出了一种新的BMST-TBCC译码算法，该算法将串行列表维特比算法（SLVA）与软校验相结合，取代了传统的循环冗余校验（CRC）。其基本思想是，与错误的候选码字相比，第一子帧的正确候选码字对第二子帧的维特比算法的输出具有较小的影响。然后通过基于引入的经验发散函数的统计学习来确定阈值。仿真结果表明，在等效译码时延的约束下，BMST-TBCC具有与极化码相当的性能。因此，BMST-TBCC可以在流超可靠和低延迟通信（URLLC）数据服务的场景中找到应用。
英文摘要：
This paper is concerned with block Markov superposition transmission (BMST) of tail-biting convolutional code (TBCC). We propose a new decoding algorithm for BMST-TBCC, which integrates a serial list Viterbi algorithm (SLVA) with a soft check instead of conventional cyclic redundancy check (CRC). The basic idea is that, compared with an erroneous candidate codeword, the correct candidate codeword for the first sub-frame has less influence on the output of Viterbi algorithm for the second sub-frame. The threshold is then determined by statistical learning based on the introduced empirical divergence function. The numerical results illustrate that, under the constraint of equivalent decoding delay, the BMST-TBCC has comparable performance with the polar codes. As a result, BMST-TBCCs may find applications in the scenarios of the streaming ultra-reliable and low latency communication (URLLC) data services.
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