Polar codes are widely used state-of-the-art codes for reliable communication that have recently been included in the 5th generation wireless standards (5G). However, there remains room for the design of polar decoders that are both efficient and reliable in the short blocklength regime. Motivated by recent successes of data-driven channel decoders, we introduce a novel $\textbf{C}$ur$\textbf{RI}$culum based $\textbf{S}$equential neural decoder for $\textbf{P}$olar codes (CRISP). We design a principled curriculum, guided by information-theoretic insights, to train CRISP and show that it outperforms the successive-cancellation (SC) decoder and attains near-optimal reliability performance on the Polar(32,16) and Polar(64,22) codes. The choice of the proposed curriculum is critical in achieving the accuracy gains of CRISP, as we show by comparing against other curricula. More notably, CRISP can be readily extended to Polarization-Adjusted-Convolutional (PAC) codes, where existing SC decoders are significantly less reliable. To the best of our knowledge, CRISP constructs the first data-driven decoder for PAC codes and attains near-optimal performance on the PAC(32,16) code.

翻译：极化码作为广泛应用于可靠通信的先进编码方案，近期已被纳入第五代无线通信标准（5G）。然而，短码长场景下高效且可靠的极化码译码器设计仍存在改进空间。受近期数据驱动信道译码器成功应用的启发，我们提出了一种新型的基于课程学习的极化码序列神经译码器（CRISP）。该模型以信息论洞察为指导，设计了具有理论依据的课程学习策略进行训练。实验表明，在Polar(32,16)与Polar(64,22)码型上，CRISP译码器超越了连续消除（SC）译码器，并达到接近最优的可靠性性能。通过与其他课程学习机制的对比，我们验证了所提课程策略对CRISP精度提升的关键作用。更重要的是，CRISP可直接扩展至极化调整卷积（PAC）码，而现有SC译码器在此类码型上可靠性显著不足。据我们所知，CRISP构建了首个针对PAC码的数据驱动译码器，在PAC(32,16)码型上实现了接近最优的性能。