Affiliation:
1. Southwest Jiaotong University
2. Peng Cheng Laboratory
3. Taiyuan University of Technology
4. Guangdong University of Technology
Abstract
Chaotic optical communication technology is considered as an effective secure communication technology, which can protect information from a physical layer and is compatible with the existing optical networks. At present, to realize long-distance chaos synchronization is still a very difficult problem, mainly because well-matched hardware cannot always be guaranteed between the transmitter and receiver. In this Letter, we introduce long short-term memory (LSTM) networks to learn a nonlinear dynamics model of an opto-electronic feedback loop, and then apply the trained deep learning model to generate a chaotic waveform for encryption and decryption at the transmitter and receiver. Furthermore, to improve the security, we establish a deep learning model pool which consists of different gain trained models and different delay trained models, and use a digital signal to drive chaos synchronization between the receiver and transmitter. The proposed scheme is experimentally verified in chaotic-encrypted 56-Gbit/s PAM-4 systems, and a decrypted performance below 7%FEC threshold (BER = 3.8×10−3) can be achieved over a 100-km fiber transmission.
Funder
National Key Research and Development Program of China
National Natural Science Foundation of China
The Major Key Project of PCL
Key Project of Sichuan Province of China
Sichuan Science and Technology Program
Fundamental Research Funds for the Central Universities
Subject
Atomic and Molecular Physics, and Optics
Cited by
34 articles.
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