基于神经网络和多目标优化算法的掺铋光纤放大器设计  

Design of Bismuth-Doped Fiber Amplifier Based on Neural Network and Multi-Objective Optimization Algorithm

作　　者：侯文强 裴丽[1,2] 王建帅[1,2] 郑晶晶[1,2] 徐文轩 田梓辰 王丁辰 王丽红[1,2] 李晶[1,2] 宁提纲[1,2] Hou Wenqiang;Pei Li;Wang Jianshuai;Zheng Jingjing;Xu Wenxuan;Tian Zichen;Wang Dingchen;Wang Lihong;Li jing;Ning Tigang(School of Electronic and Information Engineering,Beijing Jiaotong University,Beijing 100044,China;Key Laboratory of All Optical Network&Advanced Telecommunication Network of EMC,Institute of Lightwave Technology,Beijing Jiaotong University,Beijing 100044,China)

机构地区：[1]北京交通大学电子信息工程学院,北京100044 [2]北京交通大学光波技术研究所全光网络与现代通信网教育部重点实验室,北京100044

出　　处：《光学学报》2024年第2期112-119,共8页Acta Optica Sinica

基　　金：国家重点研发计划(2020YFB1805802);国家自然基金项目(62235003,62221001)。

摘　　要：掺铋光纤放大器有助于将光纤通信系统拓展至新的传输波段。然而,其增益和噪声性能存在相互制约的关系,提升增益往往会导致噪声性能的恶化,反之亦然。因此,提出一种结合反向传播神经网络(BPNN)和带精英保留策略的快速非支配排序遗传算法(NSGA-Ⅱ)的多目标优化方法,通过对两级掺铋光纤放大器结构进行设计,实现了增益和噪声性能的同时优化。使用经过训练的BPNN对增益和噪声系数预测的均方根误差分别为0.191和0.084,具有较高预测精度。以高增益和低噪声系数为目标,使用NSGA-Ⅱ算法进行优化,得到包含500个解的Pareto最优解集。优化后,放大器所能实现的平均增益范围为15~37 d B,相应的平均噪声系数范围为4.95~5.31 d B。利用BPNN代替求解耦合微分方程来评价个体适应度,使得优化时间较传统方法由106s左右降低为80 s左右,大幅提升了优化效率。所提方法也为其他掺杂光纤放大器的高效率、多目标结构优化设计提供了一种新的思路。Objective Multi-band transmission is considered an effective solution to address the increasing capacity constraints in fiber optic communication systems.However,due to the lack of mature optical amplifiers,the large-scale deployment of dense wavelength division multiplexing(DWDM)technology for long-distance transmission in bands such as O,E,and S has not yet been achieved.In recent years,researchers have discovered that different dopants in bismuth-doped silica fibers exhibit broad fluorescence characteristics in the near-infrared region.This finding brings hope for addressing the aforementioned challenges.In traditional approaches,the performance analysis of amplifiers often requires solving a set of coupled differential equations using methods such as the Runge-Kutta algorithm combined with the Shooting method or Relaxation method.When incorporating global optimization algorithms,it becomes necessary to solve thousands of related equations,resulting in a complex and time-consuming process.Previous research methods have mainly focused on the optimization design of Raman fiber amplifiers or hybrid optical amplifiers,with fewer studies specifically targeting the structural optimization design of doped fiber amplifiers,particularly bismuth-doped fiber amplifier(BDFA).Moreover,most of these studies have employed single-objective optimization algorithms,resulting in obtaining only one optimal solution at a time.In general,there is a trade-off relationship between the gain and noise performance of amplifiers.Increasing the gain often leads to the deterioration of the noise performance,and vice versa.As a result,there is no unique optimal solution.Therefore,it is necessary to design a method that can accurately model the amplifier and efficiently optimize multiple performance metrics simultaneously.Methods The backpropagation neural network(BPNN)is a type of multilayer feedforward neural network consisting of input layer,hidden layers,and output layer.The input layer contains six neurons corresponding to the input sig

关 键 词：光纤通信 掺铋光纤放大器 反向传播神经网络 多目标优化 带精英保留策略的快速非支配排序遗传算法 

分 类 号：O436[机械工程—光学工程]