Polar codes are the first class of error correcting codes that provably achieve the channel capacity at infinite code length. They were selected for use in the fifth generation of cellular mobile communications (SG). In practical scenarios such as SG, a cyclic redundancy check (CRC) is concatenated with polar codes to improve their finite length performance. This is mostly beneficial for sequential successive-cancellation list decoders. However, for parallel iterative belief propagation (BP) decoders, CRC is only used as an early stopping criterion with incremental error-correction performance improvement. In this paper, we first propose a CRC-polar BP (CPBP) decoder by exchanging the extrinsic information between the factor graph of the polar code and that of the CRe. We then propose a neural CPBP (NCPBP) algorithm which improves the CPBP decoder by introducing trainable normalizing weights on the concatenated factor graph. Our results on a SG polar code of length 128 show that at the frame error rate of 10-5 and with a maximum of 30 iterations, the error-correction performance of CPBP and NCPBP are approximately 0.25 dB and 0.5 dB better than that of the conventional CRC-aided BP decoder, respectively, while introducing almost no latency overhead.
Doan et al. further introduce in REF a neural decoder for the CRC-Polar code.
Neural Belief Propagation Decoding of CRC-Polar Concatenated Codes
ICC 2019 - 2019 IEEE International Conference on Communications (ICC)
ICC 2019 - 2019 IEEE International Conference on Communications (ICC)
Mathematics Computer Science