Belief Propagation Decoding of Polar Codes using Intelligent Post-processing

Polar codes, as only one provable capacity-achieving channel code, have become one of the 5G standards. Belief propagation (BP) algorithm has become one of popular decoding polar codes because of unique advantage of high parallelism. But compared to the successive cancellation list (SCL) algorithm, BP algorithm still has a performance gap. This paper exploits the design of polar BP decoder using intelligent post-processing to improve error performance. The post-processing is used to find the error bits and flip it as the messages of the initial frozen positions. We also use deep neural networks to improve the accuracy of finding error bits. Simulation shows that compared with the original BP decoding, the approach proposed in this work can achieve a performance improvement of about 0.3 dB with slight complexity increase.