Robust Non-Linear Feedback Coding via Power-Constrained Deep Learning-猫眼课题宝

DOI：

10.48550/arxiv.2304.13178

发表时间：

2023-04

期刊：

ArXiv

影响因子：

通讯作者：

中科院分区：

其他

文献类型：

作者：

Junghoon Kim;Taejoon Kim;D. Love;Christopher G. Brinton

反馈使能通信的代码设计一直是一个长期存在的开放问题。最近对非线性、基于深度学习的编码方案的研究表明，与线性编码相比，通信可靠性有了显著提高，但仍然容易受到信道上存在的前向和反馈噪声的影响。在本文中，我们开发了一个新的家庭的非线性反馈码，大大提高了信道噪声的鲁棒性。我们的基于自动编码器的架构旨在基于连续的比特块学习代码，这比逐比特处理具有去噪优势，有助于克服编码器和解码器在噪声信道上的物理分离。此外，我们开发了一个功率控制层的编码器显式地将硬件约束的学习优化，并证明所得到的平均功率约束是渐近满足。数值实验表明，我们的计划优于国家的最先进的反馈码的宽利润率超过实际的前向和反馈噪声制度，并提供信息理论的见解，我们的非线性代码的行为。此外，我们观察到，在一个长的块长度制度，规范纠错码仍然是最好的反馈码时，反馈噪声变得很高。

The design of codes for feedback-enabled communications has been a long-standing open problem. Recent research on non-linear, deep learning-based coding schemes have demonstrated significant improvements in communication reliability over linear codes, but are still vulnerable to the presence of forward and feedback noise over the channel. In this paper, we develop a new family of non-linear feedback codes that greatly enhance robustness to channel noise. Our autoencoder-based architecture is designed to learn codes based on consecutive blocks of bits, which obtains de-noising advantages over bit-by-bit processing to help overcome the physical separation between the encoder and decoder over a noisy channel. Moreover, we develop a power control layer at the encoder to explicitly incorporate hardware constraints into the learning optimization, and prove that the resulting average power constraint is satisfied asymptotically. Numerical experiments demonstrate that our scheme outperforms state-of-the-art feedback codes by wide margins over practical forward and feedback noise regimes, and provide information-theoretic insights on the behavior of our non-linear codes. Moreover, we observe that, in a long blocklength regime, canonical error correction codes are still preferable to feedback codes when the feedback noise becomes high.

Robust Non-Linear Feedback Coding via Power-Constrained Deep Learning

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