AI 中文总结
本文提出MPAC码及HFSC混合译码，通过陪集分析优化其最小重量码字数量，使MPAC码在相近译码复杂度下性能优于PAC码、CRC-极化码。
AI 中文摘要
本文提出了改进型极化调整卷积（MPAC）码及其混合译码方案，实现了性能与复杂度的更优权衡。对于MPAC码，仅一部分信息比特经过卷积变换，输出与剩余信息比特结合后用于内部极化变换；对应地，经卷积变换的比特采用Fano译码恢复，剩余信息比特采用连续消除（SC）译码恢复，构成混合Fano-连续消除（HFSC）译码。MPAC码通过陪集分析设计，该分析可表征最小重量码字（MWC）的数量。研究发现，部分卷积变换可通过有效利用冻结集的行组合提升码字性能，这一特性使MPAC码优于其原型极化调整卷积（PAC）码及循环冗余校验（CRC）-极化码。此外，可通过减少MWC数量优化MPAC码。数值结果表明，在相似的译码复杂度预算下，MPAC码与采用Fano译码的PAC码、采用SC列表（SCL）译码的CRC-极化码相比，具备相当的译码性能。
英文摘要
This paper proposes modified polarization-adjusted convolutional (MPAC) codes and their hybrid decoding that achieves an improved performance-complexity tradeoff. For MPAC codes, only a subset of the information bits undergo the convolutional transform. The output is then combined with the remaining information bits for the inner polar transform. Correspondingly, the convolutionally transformed bits are recovered by Fano decoding, while the remaining information bits are recovered by the successive cancellation (SC) decoding, constituting the hybrid Fano-successive cancellation (HFSC) decoding. The MPAC codes are further designed by the coset-wise analysis that characterizes the number of minimum weight codewords (MWCs). It is discovered that a partially convolutional transform can improve the codeword through utilizing the row combinations of the frozen set efficiently. This property enables the MPAC codes to outperform their prototype polarization-adjusted convolutional (PAC) codes and cyclic redundancy check (CRC)-polar codes. Furthermore, MPAC codes can be optimized by reducing the number of MWCs. Our numerical results demonstrate that, with a similar decoding complexity budget, the MPAC codes offer competent decoding performance when compared with PAC codes using Fano decoding and CRC-polar codes using SC list (SCL) decoding.
CommentsThis paper has submitted to IEEE Transactions on Information Theory
