As new deep-learned error-correcting codes continue to be introduced, it is important to develop tools to interpret the designed codes and understand the training process. Prior work focusing on the deep-learned TurboAE has both interpreted the learned encoders post-hoc by mapping these onto nearby ``interpretable'' encoders, and experimentally evaluated the performance of these interpretable encoders with various decoders. Here we look at developing tools for interpreting the training process for deep-learned error-correcting codes, focusing on: 1) using the Goldreich-Levin algorithm to quickly interpret the learned encoder; 2) using Fourier coefficients as a tool for understanding the training dynamics and the loss landscape; 3) reformulating the training loss, the binary cross entropy, by relating it to encoder and decoder parameters, and the bit error rate (BER); 4) using these insights to formulate and study a new training procedure. All tools are demonstrated on TurboAE, but are applicable to other deep-learned forward error correcting codes (without feedback).

翻译：随着新型深度学习纠错码的不断涌现，开发用于解读设计码字并理解训练过程的工具变得至关重要。以往针对深度学习的TurboAE的研究，既通过将学习编码器映射到邻近的"可解读"编码器进行事后解读，又用多种解码器实验评估了这些可解读编码器的性能。本文致力于开发用于解读深度学习纠错码训练过程的工具，重点关注：1)利用Goldreich-Levin算法快速解读学习编码器；2)将傅里叶系数作为理解训练动态和损失景观的工具；3)通过关联编码器与解码器参数及误码率，重新构建训练损失函数（二元交叉熵）；4)基于这些见解提出并研究新的训练流程。所有工具均在TurboAE上验证，但可推广至其他深度学习前向纠错码（无反馈场景）。