  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).
