Deep Chaos Synchronization

TL;DR

This paper introduces a Deep Chaos Synchronization system using CNN that effectively synchronizes chaotic signals over noisy channels without extensive training data, outperforming traditional RNN methods in robustness and convergence.

Contribution

The paper presents a novel CNN-based DCS system that requires no prior data and offers competitive performance against RNN-based methods for chaotic synchronization.

Findings

DCS is robust against noise and converges efficiently.

DCS performs comparably to RNN-based systems.

Potential applications in URLLC and IIoT environments.

Abstract

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).