2 research outputs found
Statistical Learning Aided Decoding of BMST of Tail-Biting Convolutional Code
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.Comment: 5 pages, 6 figures, submitted to ISIT201
Successive Cancellation List Decoding of Semi-random Unit Memory Convolutional Codes
We present in this paper a special class of unit memory convolutional codes
(UMCCs), called semi-random UMCCs (SRUMCCs), where the information block is
first encoded by a short block code and then transmitted in a block Markov
(random) superposition manner. We propose a successive cancellation list
decoding algorithm, by which a list of candidate codewords are generated
serially until one passes an empirical divergence test instead of the
conventional cyclic redundancy check (CRC). The threshold for testing the
correctness of candidate codewords can be learned off-line based on the
statistical behavior of the introduced empirical divergence function (EDF). The
performance-complexity tradeoff and the performance-delay tradeoff can be
achieved by adjusting the statistical threshold and the decoding window size.
To analyze the performance, a closed-form upper bound and a simulated lower
bound are derived. Simulation results verify our analysis and show that: 1) The
proposed list decoding algorithm with empirical divergence test outperforms the
sequential decoding in high signal-to-noise ratio (SNR) region; 2) Taking the
tail-biting convolutional codes (TBCC) as the basic codes, the proposed list
decoding of SRUMCCs have comparable performance with the polar codes under the
constraint of equivalent decoding delay.Comment: Submitted to IEEE Transactions on Information Theory. arXiv admin
note: substantial text overlap with arXiv:1902.0980