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：极化码是首类能够以高效编译码方式达到二进制信道对称容量的结构化信道码。2019年，Arikan提出了一种新型极化编码方案——极化调整卷积码。与极化码相比，PAC码在极化编码前先使用卷积码对信息字进行预编码。这使得PAC码在Fano序列译码和逐次抵消列表译码下均比极化码获得显著编码增益。鉴于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较Fano译码具有优势，本文聚焦SCL译码并提出一种适用于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，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码在保持与SCL译码几乎相同性能的同时，具有更低的译码延迟。