Statistical Learning Aided List Decoding of Semi-Random Block Oriented Convolutional Codes
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In this paper, we propose a statistical learning aided list decoding algorithm, which integrates a serial list Viterbi algorithm (SLVA) with a soft check instead of the conventional cyclic redundancy check (CRC), for semi-random block oriented convolutional codes (SRBO-CCs). The basic idea is that, compared with an erroneous candidate codeword, the correct candidate codeword for the first sub-frame has less effect on the output of Viterbi algorithm (VA) for the second sub-frame. The threshold for testing the correctness of the candidate codeword is then determined by learning the statistical behavior of the introduced empirical divergence function (EDF). With statistical learning aided list decoding, the performance-complexity tradeoff and the performance-delay tradeoff can be achieved by adjusting the statistical threshold and extending the decoding window, respectively. 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 statistical learning aided list decoding outperforms the sequential decoding in high signal-to-noise ratio (SNR) region; 2) under the constraint of equivalent decoding delay, the SRBO-CCs have comparable performance with the polar codes.
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