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的先进可靠通信编码方案，近期已被纳入第五代无线通信标准（5G）。然而，在短码长场景下设计兼具高效性与可靠性的极化码译码器仍存在探索空间。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基于课程学习的极化码序贯神经译码器（CRISP）。通过信息论启发的理论指导，我们设计了具有原则性的课程学习策略来训练CRISP，实验表明该译码器在Polar(32,16)和Polar(64,22)码上显著优于连续消除（SC）译码器，并达到近最优可靠性性能。通过与其他课程学习策略的对比实验，我们证明了所提出的课程设计对实现CRISP精度增益具有关键作用。更值得注意的是，CRISP可便捷地扩展至极化调整卷积（PAC）码——现有SC译码器在该类码上表现显著欠佳。据我们所知，CRISP首次构建了PAC码的数据驱动译码器，并在PAC(32,16)码上实现了近最优性能。