# Statistical Learning Aided Decoding of BMST of Tail-Biting Convolutional Code

## TL;DR

This paper introduces a statistical learning-based decoding algorithm for BMST of tail-biting convolutional codes, improving decoding performance by replacing CRC with a soft check and leveraging empirical divergence functions.

## Contribution

It proposes a novel decoding method combining SLVA with a soft check and statistical learning, enhancing BMST-TBCC decoding without CRC.

## Key findings

- BMST-TBCC achieves performance comparable to polar codes.
- The proposed decoding method improves error correction with lower latency.
- Numerical results validate the effectiveness of the statistical learning approach.

## Abstract

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.