复杂信号驱动半导体激光器混沌振荡的神经网络学习  

Neural Network Learning for Chaotic Oscillations in Semiconductor Lasers Driven by Complex Signals

作　　者：范小琦 毛晓鑫 王安帮 Fan Xiaoqi;Mao Xiaoxin;Wang Anbang(Key Lab of Advanced Transducers and Intelligent Control,Ministry of Education,Taiyuan University of Technology,Taiyuan 030024,Shanxi,China;College of Electronic Information and Optical Engineering,Taiyuan University of Technology,Taiyuan 030024,Shanxi,China;Key Lab of Photonic Technology for Integrated Sensing and Communication,Ministry of Education,Guangdong University of Technology,Guangzhou 510006,Guangdong,China;Guangdong Provincial Key Lab of Photonics Information Technology,Guangdong University of Technology,Guangzhou 510006,Guangdong,China;Institute of Advanced Photonics Technology,School of Information Engineering,Guangdong University of Technology,Guangzhou 510006,Guangdong,China)

机构地区：[1]太原理工大学新型传感器与智能控制教育部重点实验室,山西太原030024 [2]太原理工大学电子信息与光电工程学院,山西太原030024 [3]广东工业大学通感融合光子技术教育部重点实验室,广东广州510006 [4]广东工业大学广东省信息光子技术重点实验室,广东广州510006 [5]广东工业大学信息工程学院先进光子技术研究院,广东广州510006

出　　处：《光学学报》2024年第21期175-182,共8页Acta Optica Sinica

基　　金：国家自然科学基金(62035009);中央引导地方科技发展基金(YDZJSX2021A009);应用光学国家重点实验室开放基金(SKLAO2022001A09);广东省引进创新创业团队计划。

摘　　要：结合卷积层的双向长短期记忆网络构建了从驱动信号到半导体激光器输出信号的映射,探究了在共同信号驱动的条件下,神经网络输出与激光器输出二者之间的混沌同步条件,揭示了神经网络性能与其网络参数、混沌信号带宽以及驱动与响应间同步性的关系。通过仿真研究发现,当驱动与响应间的互相关系数超过0.67时,神经网络对于7.91 GHz带宽的混沌可以在背靠背条件下产生约0.9234相关系数的同步混沌。当进一步增大混沌带宽至9.2 GHz以上或者降低驱动响应间相关系数至0.63以下时,通过窃听公共信道中的驱动信号并结合神经网络攻击,能够获得与混沌载波相关系数为0.85的时序。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 shortterm 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 findi

关 键 词：半导体激光器 混沌激光 混沌同步 神经网络 保密通信 

分 类 号：TN248.4[电子电信—物理电子学]