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In this Letter, a list decoding algorithm is proposed for the semi-random block-oriented convolutional code (SRBO-CC). To decode each sub-frame, a list of candidates are serially computed until finding a qualified one or achieving maximum list, where the correctness of the decoding candidate is checked by a statistical threshold. Simulation results show that SRBO-CC with list decoding is competitive with polar codes and that the performance-complexity tradeoffs can be achieved by adjusting the statistical threshold.

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.Index Terms-Block Markov superposition transmission (BMST), list decoding, statistical learning, ultra-reliable and low latency communication (URLLC).

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