A soft-output stack decoding of polarization-adjusted convolutional codes
Zuokai Jiang, Zhiliang Huang, Youyan Zhang, Baohong Zhou · 2023
Recently, Arikan has proposed a new scheme to transfer the error correction part to external codes for encoding, namely polarization-adjusted convolutional (PAC) coding. However, all existing decoding methods of current research for PAC codes focused on the classic sequential decoding-Fano decoding. The stack decoding which is another well-known sequential decoding, has not been reported. In this paper, we try to do a thorough study of the stack decoding for PAC codes. First, we find that the traditional stack decoding (SD) is not very effective for the PAC codes, especially in terms of complexity. Therefore, we propose a soft output stack decoding (SOSD) for PAC codes, which can reduce the computation very much. In the SOSD algorithm, the discarded paths are recorded and used in the final decision of decoding codeword. The SOSD algorithm can reduce the stack size (amount of computation) very much with almost no decoding performance loss. By carefully choosing the stack size and soft output threshold, in the case of a PAC code with code length 128 and rate 1/2, the SOSD algorithm can reduce 82% of the computation by comparing the conventional SD algorithm.