  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.
