Open AccessTime delay estimation from the time series for optical chaos systems using deep learning
Author(s) -
Xiaojing Gao,
Wei Zhu,
Qi Yang,
Deze Zeng,
Lei Deng,
Qing Chen,
Mengfan Cheng
Publication year - 2021
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.419654
Subject(s) - computer science , chaotic , autocorrelation , synchronization (alternating current) , noise (video) , artificial neural network , nonlinear system , time series , optical chaos , chaos (operating system) , convolutional neural network , artificial intelligence , semiconductor laser theory , laser , machine learning , physics , telecommunications , optics , statistics , mathematics , channel (broadcasting) , computer security , quantum mechanics , image (mathematics)
We propose a model-free time delay signature (TDS) extraction method for optical chaos systems. The TDS can be identified from time series without prior knowledge of the actual physical processes. In optical chaos secure communication systems, the chaos carrier is usually generated by a laser diode subject to opto-electronic/all-optical time delayed feedback. One of the most important factors to security considerations is the concealment of the TDS. So far, statistical analysis methods such as autocorrelation function (ACF) and delayed mutual information (DMI) are usually used to unveil the TDS. However, the effectiveness of these methods will be reduced when increasing the nonlinearity of chaos systems. Meanwhile, certain TDS concealment strategies have been designed against statistical analysis. In our previous work, convolutional neural network shows its effectiveness on TDS extraction of chaos systems with high loop nonlinearity. However, this method relies on the knowledge of detailed structure of the chaos systems. In this work, we formulate a blind identification method based on long short-term memory neural network (LSTM-NN) model. The method is validated against the two major types of optical chaos systems, i.e. opto-electronic oscillator (OEO) chaos system and laser chaos system based on internal nonlinearity. Moreover, some security enhanced chaotic systems are also studied. The results show that the proposed method has high tolerance to additive noise. Meanwhile, the data amount needed is less than existing methods.