基于神经网络和方程数值解的FRA增益控制方法
Gain Control Method for FRA based on Neural Network and Numerical Solution of Equations
穆宽林 1武岳 1周健 1殷仕淑1
作者信息
- 1. 安徽财经大学 管理科学与工程学院,安徽 蚌埠 233030
- 折叠
摘要
[目的]基于多泵浦技术的光纤拉曼放大器(FRA)拥有噪声低、增益带宽大和增益谱形状可控的特点,是长距离光纤传输网络系统的理想光中继放大器.在动态光纤传输网络系统中需要增益自适应可控的智能光放大器,文章介绍了一种基于神经网络和拉曼功率耦合方程数值解的 FRA增益控制方法.[方法]首先,收集包含 FRA中信号光增益值、泵浦光功率和波长值的数据集来训练神经网络,建立 FRA中信号光增益和泵浦光参数的近似映射关系;然后,利用训练后的神经网络根据信号光的目标增益值确定 FRA初始泵浦光功率和波长值;最后,通过求解拉曼功率耦合方程数值解的方法优化泵浦光功率值,达到提升 FRA输出信号光增益准确度的目的.[结果]文章对所使用的训练数据集中各组信号光增益平坦度对最终 FRA输出信号光增益准确度的影响进行了研究.仿真结果显示,所使用训练数据集中各组信号光增益波动越小,FRA输出增益准确度越高.当训练数据中各信号光增益波动低于 2dB时,FRA输出的 1 000 组检验信号光增益的均方根误差(RMSE)的均值和方差分别为 0.230 和 0.010 dB,增益最大误差的均值和方差分别为 0.462 和 0.044 dB.[结论]以上结果说明,文章所述方法可以实现高准确度的 FRA增益控制,该方法为动态光纤传输网络中智能光放大器增益自适应控制的研究提供了新的思路和方法.
Abstract
[Objective]Fiber Raman Amplifier(FRA)based on multi-pump technology has features of low noise,a wide gain bandwidth,and a controllable gain spectrum shape,which is regarded as an ideal optical relay amplifier for long-haul fiber optic transmission network systems.Intelligent optical amplifiers with adaptive controllable gain are required in dynamic fiber optic transmission network systems.This article introduces a gain control method for FRA based on neural network and numerical solutions of Raman power coupling equations.[Methods]First,the data set containing the signal gains,pump powers and wavelengths in the FRA is collected to train the neural network to establish an approximate mapping relationship between the signal gains and pump parameters.Subsequently,the trained neural network is utilized to determine the initial pump powers and wavelengths of the FRA based on the target gains of the signal.Finally,the pump powers are optimized by solving the nu-merical solutions of the Raman power coupling equations to improve the accuracy of the FRA output signal gains.[Results]The paper investigates the effect of the flatness of signal gains in each group in the training dataset on the accuracy of FRA output signal gains.When the gain fluctuation of each group signal in the training data is less than 2 dB,the mean and variance of the Root Mean Square Error(RMSE)of the 1 000 sets of test signal gains output by the FRA are 0.230 and 0.010 dB,respective-ly.Additionally,the mean and variance of the maximum error of the gains are 0.462 and 0.044 dB,respectively.[Conclusion]The results indicate that the proposed method can achieve high-precision FRA gain control,offering a new idea and method for investigating intelligent optical amplifier gain adaptive control in dynamic fiber optic transmission networks.
关键词
光纤拉曼放大器/神经网络/方程数值解/增益Key words
FRA/neural network/numerical solution of equations/gain引用本文复制引用
基金项目
安徽省高校自然科学研究资助项目(KJ2021A0479)
安徽财经大学科研资助项目(ACKYC22082)
出版年
2024
国家科技期刊平台
NSTL
万方数据