Inventing Codes for Channels With Active Feedback via Deep Learning
Inventing Codes for Channels With Active Feedback via Deep Learning
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DOI：
10.1109/jsait.2022.3216515
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发表时间：
2022-09
期刊：
影响因子：
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通讯作者：
Karl Chahine;Rajesh K. Mishra;Hyeji Kim
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文献类型：
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作者：
Karl Chahine;Rajesh K. Mishra;Hyeji Kim
为有反馈的信道设计可靠的编码是编码理论中长期存在的开放性问题之一，具有重要的理论和实践意义。虽然先前有许多关于带反馈通道的解析码的工作，但其中大多数都集中于具有无噪声输出反馈的通道，其中最佳编码方案仍然未知。对于具有噪声反馈的通道，推导分析代码变得更具挑战性，并且了解的也少得多。最近，事实证明深度学习可以在一定程度上解决这些挑战，并导致发现具有噪声输出反馈的通道的新代码。尽管取得了成功，但仍然存在三个重要的开放问题： $(a)$ 基于深度学习的代码主要集中在被动反馈设置上，这被证明比主动反馈设置更糟糕； $(b)$基于深度学习的代码难以解释或分析； $(c)$ 它们尚未在无线渠道中成功演示并提供反馈。我们解决这三个挑战。首先，我们提出了一个基于学习的框架，用于为具有主动反馈的渠道设计代码。其次，我们分析所学习代码的潜在特征，以设计分析编码方案。我们证明了近似的分析代码是最先进代码的一个重要变体，这表明深度学习是一个强大的工具，可以为具有挑战性的通信场景导出新的分析通信方案。最后，我们通过构建一个无线测试台来演示神经代码的无线性能，该测试台由两个独立的 N200 USRP 组成，分别充当发射器和接收器。据我们所知，这是交互式通道神经代码的第一个无线硬件实现。
Designing reliable codes for channels with feedback, which has significant theoretical and practical importance, is one of the long-standing open problems in coding theory. While there are numerous prior works on analytical codes for channels with feedback, the majority of them focus on channels with noiseless output feedback, where the optimal coding scheme is still unknown. For channels with noisy feedback, deriving analytical codes becomes even more challenging, and much less is known. Recently, it has been shown that deep learning can, in part, address these challenges and lead to the discovery of new codes for channels with noisy output feedback. Despite the success, there are three important open problems: $(a)$ deep learning-based codes mainly focus on the passive feedback setup, which is shown to be worse than the active feedback setup; $(b)$ deep learning-based codes are hard to interpret or analyze; and $(c)$ they have not been successfully demonstrated in the over-the-air channels with feedback. We address these three challenges. First, we present a learning-based framework for designing codes for channels with active feedback. Second, we analyze the latent features of the learned codes to devise an analytical coding scheme. We show that the approximated analytical code is a non-trivial variation of the state-of-the-art codes, demonstrating that deep learning is a powerful tool for deriving a new analytical communication scheme for challenging communication scenarios. Finally, we demonstrate the over-the-air performance of our neural codes by building a wireless testbed that consists of two separate N200 USRPs operating as the transmitter and the receiver. To the best of our knowledge, this is the first over-the-air hardware implementation of neural codes for interactive channels.