基于深度学习的光电振荡混沌系统建模及FPGA应用  

Modeling and FPGA Application of Optoelectronic Oscillation Chaotic System Based on Deep Learning

作　　者：张卓宇 蒋林 陈博阳 冯国豪 冯家城 闫连山[1,2] Zhang Zhuoyu;Jiang Lin;Chen Boyang;Feng Guohao;Feng Jiacheng;Yan Lianshan(School of Information Science and Technology,Southwest Jiaotong University,Chengdu 611756,Sichuan,China;Yantai Research Institute of New Generation Information Technology,Southwest Jiaotong University,Yantai 264001,Shandong,China)

机构地区：[1]西南交通大学信息科学与技术学院,四川成都611756 [2]西南交通大学烟台新一代信息技术研究院,山东烟台264001

出　　处：《光学学报》2024年第19期109-119,共11页Acta Optica Sinica

基　　金：国家重点研发计划(2021YFB2206303);国家自然科学基金(U22A2089);山东省重点研发计划项目(2023CXPT100);四川省重点研发计划项目(2023YFG0143)。

摘　　要：为了解决传统混沌加密通信系统收发端宽带混沌同步困难的问题,基于长短期记忆(LSTM)网络对光电振荡混沌源进行建模。将混沌人工智能(AI)模型优化剪枝后部署到现场可编程门阵列(FPGA)上,以70 MHz的采样频率驱动数模转换(DAC)芯片实时输出混沌波形。与优化前相比,FPGA上的DSP模块资源占用减少了31.7%,Block RAM(BRAM)资源占用减少了58%,计算延时降低了44.4%。通过绘制相图以及添加微小扰动的方法证明了部署后的模型可保持原始光电振荡混沌系统的输出特性。此外,进一步基于最低有效位选择优化的后处理方法,将部署的混沌AI模型用于实时随机数产生,随机数产生速率为70 Mbit/s,所得结果通过了NIST SP 800-22测试。Objective As the cornerstone of network information transmission,the optical fiber communication network currently carries more than 90%of the global data traffic transmission,and its security is vital to maintaining information transmission privacy.Optical fiber communication is traditionally considered to be relatively secure.However,with the progress of communication technology,such as the application of wavelength division multiplexing and optical amplification technology,despite the greatly improved transmission capacity and distance,concerns about the potential eavesdropping risk of optical fiber communication systems are caused.At present,secure communication technology is mainly divided into two categories of mathematical algorithm encryption and physical layer encryption,with more attention paid to the latter category because of its ability to face high performance computing threats.Meanwhile,quantum key distribution provides absolute security in theory,but faces technical obstacles in implementation,such as low efficiency of single photon detection and large transmission loss,thus limiting its practicability.As a method of physical layer encryption,chaotic secure communication employs the randomness of chaotic optical signals to encrypt information.However,it is a big challenge to realize wide-band chaotic synchronization in high-speed systems,mainly because the initial value sensitivity of chaotic systems makes it difficult to accurately match the parameters of the receiving and sending terminals.To solve the difficulty of chaos synchronization at the receiving and sending ends of traditional chaotic encryption communication systems,some studies have proposed to adopt deep learning technology to realize chaos generation and synchronization,but most of the current studies are only offline processing on computers.Therefore,we propose a method based on deep learning technology to model the optoelectronic oscillation chaotic source to realize the digital domain generation of chaos.Additionally,the chaotic

关 键 词：混沌光通信 随机数生成 长短期记忆网络 光电振荡器 现场可编程门阵列 

分 类 号：TN929.11[电子电信—通信与信息系统]