Deep learning methods have recently been used to construct non-linear codes for the additive white Gaussian noise (AWGN) channel with feedback. However, there is limited understanding of how these black-box-like codes with many learned parameters use feedback. This study aims to uncover the fundamental principles underlying the first deep-learned feedback code, known as Deepcode, which is based on an RNN architecture. Our interpretable model based on Deepcode is built by analyzing the influence length of inputs and approximating the non-linear dynamics of the original black-box RNN encoder. Numerical experiments demonstrate that our interpretable model -- which includes both an encoder and a decoder -- achieves comparable performance to Deepcode while offering an interpretation of how it employs feedback for error correction.

翻译：深度学习方法近期被用于构造带反馈的加性高斯白噪声（AWGN）信道的非线性编码。然而，这些具有大量学习参数的黑盒式编码如何利用反馈信号，目前仍缺乏深入理解。本研究旨在揭示首个基于RNN架构的深度学习反馈码——Deepcode——的基本原理。通过分析输入的影响长度并近似原始黑盒RNN编码器的非线性动力学特性，我们构建了基于Deepcode的可解释模型。数值实验表明，我们的可解释模型（包含编码器与解码器）在性能上与Deepcode相当，同时揭示了该模型如何利用反馈进行纠错的内在机制。