Neural Network Learning for Chaotic Oscillations in Semiconductor Lasers Driven by Complex Signals
Objective Chaotic secure communication offers advantages such as high speed and compatibility with existing fiber optical systems. It has emerged as a primary encryption method for optical communication,with enhancing transmission rates and distances in chaotic optical communication systems becoming a key research focus in recent years. Fiber optical links are typically affected by linear effects,nonlinear Kerr effects,and amplifier noise from erbium-doped optical fiber amplifiers,which present challenges for advancing chaotic secure communications. Achieving high-quality chaos synchronization remains difficult,further hindering progress in this field. Neural networks have been explored for constructing chaos synchronization in optoelectronic oscillator systems. However,recovering synchronized chaotic carriers from signals mixed with messages and chaotic carriers is challenging,as message content can affect synchronization quality. Moreover,substituting hardware-matched synchronization with neural networks may reduce physical layer security. Therefore,there is an urgent need to explore new methods for synchronizing chaotic carriers between semiconductor laser outputs and neural networks,while ensuring system security. In this paper,we utilize a long and short-term memory network with a convolutional layer to synchronize a semiconductor laser system driven by a common signal.Methods The output of a distributed feedback (DFB) semiconductor laser driven by a common chaotic signal is selected as the subject of research. The driving signal serves as the input vector for the neural network,while the laser's response output is used as the response vector for training the neural network. Subsequently,the neural network parameters are adjusted to achieve optimal network performance. The input signal's signal-to-noise ratio is varied to assess the neural network model's tolerance to noise. Additionally,variations in chaos carrier bandwidth and driver-response correlation are employed to train the neural network. Based on these findings,a range of synchronization parameters is derived for implementing a common driven synchronization system using neural networks.Results and Discussions By correlating the neural network output signal with the response laser output signal,the following results are obtained:1) under conditions of injection intensity 0.156 and frequency detuning 12 GHz,the correlation coefficient between the drive and response signals is approximately 0.67,and the correlation coefficient between the neural network output and the response laser output can reach up to 0.9234. The chaotic attractor structure is effectively reproduced,with consistent spectral characteristics;the 80％ energy bandwidth is about 7.9 GHz (Fig. 3). 2) When the signal-to-noise ratio exceeds 8 dB,the correlation between the neural network model output and the response laser output could reach 0.9 (Fig. 5),demonstrating the robustness of the neural network model. 3) As the complexity of the chaotic signals increases,so does the decrease in the correlation coefficient of the neural network model output. (Fig. 6). For chaos bandwidths in secure communication systems exceeding 9 GHz,the effectiveness of the current model diminishes in constructing the drive-response mapping,indicating potential resistance to neural network attacks. 4) When the correlation coefficient between the drive and response signals exceed 0.65,the neural network could construct a mapping output with a correlation coefficient higher than 0.9 (Fig. 7).Conclusions Simulation studies employ neural networks to simulate the chaotic output of semiconductor lasers. These studies analyze the correlation between them,using a 7.91 GHz chaotic signal as an example to assess the impact of neural network parameters. The highest achieved correlation coefficient for chaotic output currently stands at 0.9234. Further analysis explores the influence of chaotic bandwidth and the correlation between driving responses on neural network synchronization performance. Feasible parameter conditions are established for synchronizing semiconductor laser chaotic output with neural network output and for safely resisting neural network attacks. Specifically,when the chaotic bandwidth exceeds 9.2 GHz,the neural network's ability to generate synchronized chaos exhibits a correlation below 0.85. Additionally,when the correlation of the driving response falls below 0.65,the correlation between the neural network output and the laser response output decreases rapidly,dropping below 0.6 and 0.8,respectively. These findings provide security insights for signal-driven semiconductor laser synchronization in secure optical communication systems and pave the way for implementing neural network-assisted chaotic synchronization with semiconductor lasers.
semiconductor laserchaos laserchaos synchronizationneural networksecure communication
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