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32 Gb/s chaotic optical communications by deep-learning-based chaos synchronization.

Junxiang Ke, Lilin Yi, Zhao Yang

|November 28, 2019

Summary

Researchers developed a deep-learning method for chaos synchronization in optical communications, enabling high-speed, secure data transmission. This simplifies receivers and overcomes previous limitations in chaotic optical networking.

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Area of Science:

Optics
Communications Engineering
Artificial Intelligence

Background:

Chaotic optical communications offer high physical layer security but face challenges in high-speed synchronization and network implementation.
Previous experimental demonstrations of high-speed chaotic optical communications are limited due to synchronization difficulties.

Purpose of the Study:

To overcome limitations in high-speed chaotic optical communication and networking.
To enable wideband chaos synchronization in the digital domain using a deep-learning approach.
To simplify chaotic receivers while maintaining security.

Main Methods:

A deep-learning-based scheme was employed to learn the nonlinear model of a chaotic transmitter.
Digital domain chaos synchronization was achieved by leveraging the learned model.
Experimental demonstration of data transmission over a fiber link.

Main Results:

Wideband chaos synchronization was successfully realized in the digital domain.
A simplified chaotic receiver design was achieved.
A 32 Gb/s message transmission over a 20 km fiber link was experimentally demonstrated.

Conclusions:

The proposed deep-learning-based chaos synchronization method effectively overcomes previous limitations in chaotic optical communications.
This approach enables a new direction for developing high-speed chaotic optical communication systems and networks.
The method enhances both the speed and security of optical communication systems.