Polar codes are the first class of structured channel codes that achieve the symmetric capacity of binary channels with efficient encoding and decoding. In 2019, Arikan proposed a new polar coding scheme referred to as polarization-adjusted convolutional (PAC)} codes. In contrast to polar codes, PAC codes precode the information word using a convolutional code prior to polar encoding. This results in material coding gain over polar code under Fano sequential decoding as well as successive cancellation list (SCL) decoding. Given the advantages of SCL decoding over Fano decoding in certain scenarios such as low-SNR regime or where a constraint on the worst case decoding latency exists, in this paper, we focus on SCL decoding and present a simplified SCL (SSCL) decoding algorithm for PAC codes. SSCL decoding of PAC codes reduces the decoding latency by identifying special nodes in the decoding tree and processing them at the intermediate stages of the graph. Our simulation results show that the performance of PAC codes under SSCL decoding is almost similar to the SCL decoding while having lower decoding latency.

翻译：极化码是第一类能够实现二进制信道对称容量且具有高效编码译码结构化的信道编码方案。2019年，Arikan提出了一种新型极化编码方案——极化调整卷积码。与传统极化码不同，PAC码在极化编码前使用卷积码对信息序列进行预编码。这种设计使得PAC码在采用Fano序贯译码以及逐次消除列表译码时，相比极化码可获得显著的编码增益。考虑到SCL译码在低信噪比场景或存在最差译码延迟约束等情况下相较于Fano译码的优势，本文聚焦于SCL译码，提出了一种适用于PAC码的简化SCL译码算法。PAC码的SSCL译码通过识别译码树中的特殊节点并在图的中间阶段对其进行处理，从而降低译码延迟。仿真结果表明，采用SSCL译码的PAC码性能与SCL译码几近相同，同时具有更低的译码延迟。