论文标题
酥脆：基于课程的顺序神经解码器，用于极地代码家族
CRISP: Curriculum based Sequential Neural Decoders for Polar Code Family
论文作者
论文摘要
极地代码是最近已包含在第五代无线标准（5G）中的可靠通信的最新代码。但是，在短距离制度中既有效又可靠的极性解码器的设计空间。通过数据驱动的通道解码器的最新成功激励，我们介绍了一种新颖的$ \ textbf {c} $ ur $ \ textbf {ri} $ culum $ \ culum $ \ textbf {s} $ equential神经解码器$ \ \ \ textbf {p textbf {p}我们设计了一个有原则的课程，以信息理论见解为指导，以训练Crisp，并表明它表现出色（SC）解码器，并在Polar（32,16）和Polar（64,22）代码上达到近乎最佳的可靠性性能。正如我们通过与其他课程进行比较所表明的那样，建议的课程的选择对于实现酥脆的准确性提高至关重要。更值得注意的是，清晰可容易地扩展到极化调整后的跨跨跨识别（PAC）代码，其中现有的SC解码器的可靠性明显降低。据我们所知，Crisp构建了PAC代码的第一个数据驱动的解码器，并在PAC（32,16）代码上达到了近乎最佳的性能。
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.
