In this study, we address the problem of chaotic synchronization over a noisy channel by introducing a novel Deep Chaos Synchronization (DCS) system using a Convolutional Neural Network (CNN). Conventional Deep Learning (DL) based communication strategies are extremely powerful but training on large data sets is usually a difficult and time-consuming procedure. To tackle this challenge, DCS does not require prior information or large data sets. In addition, we provide a novel Recurrent Neural Network (RNN)-based chaotic synchronization system for comparative analysis. The results show that the proposed DCS architecture is competitive with RNN-based synchronization in terms of robustness against noise, convergence, and training. Hence, with these features, the DCS scheme will open the door for a new class of modulator schemes and meet the robustness against noise, convergence, and training requirements of the Ultra Reliable Low Latency Communications (URLLC) and Industrial Internet of Things (IIoT).

中文翻译：

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深层混沌同步

在这项研究中，我们通过引入使用卷积神经网络（CNN）的新型深层混沌同步（DCS）系统，解决了在噪声通道上的混沌同步问题。基于常规深度学习（DL）的通信策略非常强大，但是对大数据集的训练通常是困难且耗时的过程。为了应对这一挑战，DCS不需要先验信息或大数据集。此外，我们提供了一种新颖的基于递归神经网络（RNN）的混沌同步系统进行比较分析。结果表明，在抗噪声，收敛和训练的鲁棒性方面，所提出的DCS体系结构与基于RNN的同步具有竞争优势。因此，有了这些功能，