A Novel Post-Processing Method for Belief Propagation List Decoding of Polar Codes.

For polar codes, the statistical breakdown of belief propagation (BP) decoding errors is firstly proposed by Sun et al. The errors are classified into three categories: unconverged errors, false converged errors and oscillation errors, which are corrected by three different post-processing methods. As for the BP List (BPL) decoding, we discover that one type of error can be transformed to another type by different factor graphs in the failed BP decoding. Besides, false converged error is usually easier to be detected and modified. Therefore, we only target false converged error to detect and correct. In this work, we propose a two-level detection rule for false converged errors and its corresponding post-processing algorithm to modify these errors in order to improve the performance of the BPL decoding. Numerical results show that the error-correction performance of the proposed decoder is more than 0.85dB better than that of the permuted BPL (PBPL) decoder with slight extra computation complexity at the frame error rate (FER) of $10^{-5}$ for 5G (1024,512) polar code. [ABSTRACT FROM AUTHOR]