This paper focuses on the frozen set design for precoded polar codes decoded by the successive cancellation list (SCL) algorithm. We propose a novel frozen set design method, whose computational complexity is low due to the use of analytical bounds and constrained frozen set structure. We derive new bounds based on the recently published complexity analysis of SCL with near maximum-likelihood (ML) performance. To predict the ML performance, we employ the state-of-the-art bounds relying on the code weight distribution. The bounds and constrained frozen set structure are incorporated into the genetic algorithm to generate optimized frozen sets with low complexity. Our simulation results show that the constructed precoded polar codes of length 512 have a superior frame error rate (FER) performance compared to the state-of-the-art codes under SCL decoding with various list sizes.

翻译：本文聚焦于采用串行抵消列表（SCL）算法解码的预编码极化码的冻结集设计。我们提出了一种新颖的冻结集设计方法，由于采用解析边界与约束冻结集结构，其计算复杂度较低。基于近期发表的近最大似然性能SCL算法的复杂度分析，我们推导出新的边界。为预测最大似然性能，我们采用依赖码重分布的现有最优边界。将边界与约束冻结集结构融入遗传算法，生成了低复杂度优化的冻结集。仿真结果表明，所构造的512长度预编码极化码在不同列表大小的SCL解码下，其帧错误率性能优于现有最优码。