Deep Chaos Synchronization

• URL: http://arxiv.org/abs/2104.08436v1
• Date: Sat, 17 Apr 2021 03:57:53 GMT
• Title: Deep Chaos Synchronization
• Authors: Majid Mobini, Georges Kaddoum (Senior Member, IEEE)
• Abstract summary: We introduce a novel Deep Chaos Synchronization (DCS) system using a Convolutional Neural Network (CNN) We also provide a novel Recurrent Neural Network (RNN)-based chaotic synchronization system for comparative analysis.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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).

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