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).

本研究利用 Goldreich-Levin 算法、傅里叶系数等工具对 TurboAE 进行深度学习的纠错编码的训练过程进行解释及理解，得出了新的训练过程，为其他深度学习的码结构提供了启示。

深度学习纠错码的训练方面解释

Interpreting Training Aspects of Deep-Learned Error-Correcting Codes

Designing codes that combat the noise in a communication medium has remained
a significant area of research in information theory as well as wireless
communications. Asymptotically optimal channel codes have been developed by
mathematicians for communicating under canonical models after over 60 years of
research. On the other hand, in many non-canonical channel settings, optimal
codes do not exist and the codes designed for canonical models are adapted via
heuristics to these channels and are thus not guaranteed to be optimal. In this
work, we make significant progress on this problem by designing a fully
end-to-end jointly trained neural encoder and decoder, namely, Turbo
Autoencoder (TurboAE), with the following contributions: ($a$) under moderate
block lengths, TurboAE approaches state-of-the-art performance under canonical
channels; ($b$) moreover, TurboAE outperforms the state-of-the-art codes under
non-canonical settings in terms of reliability. TurboAE shows that the
development of channel coding design can be automated via deep learning, with
near-optimal performance.

本文介绍了一种新型神经编码器和解码器 TurboAE，该编码器可以通过深度学习来自动化设计通道编码，以提高非规范通道模型的可靠性，并在中等块长度下接近规范通道模型的最新性能水平。