  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.
