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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
- 光通信研究 2024年第5期 页码：23005101-23005106
- 作者机构：
安徽财经大学 管理科学与工程学院，安徽 蚌埠 233030
- 作者简介：
穆宽林，讲师。E-mail：mukuanlin@126.com
- 基金信息：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)
DOI：10.13756/j.gtxyj.2024.230051
中图分类号： TN929.11纸质出版日期：2024-10-10，
收稿日期：2023-04-13，
修回日期：2023-05-04，
- 稿件说明：
移动端阅览
穆宽林，武岳，周健，等. 基于神经网络和方程数值解的FRA增益控制方法[J].光通信研究，2024(4)：230051.
Mu K L, Wu Y，Zhou J，et al. Gain Control Method for FRA based on Neural Network and Numerical Solution of Equations[J]. Study on Optical Communications，2024(4)：230051.
穆宽林，武岳，周健，等. 基于神经网络和方程数值解的FRA增益控制方法[J].光通信研究，2024(4)：230051. DOI： 10.13756/j.gtxyj.2024.230051.
Mu K L, Wu Y，Zhou J，et al. Gain Control Method for FRA based on Neural Network and Numerical Solution of Equations[J]. Study on Optical Communications，2024(4)：230051. DOI： 10.13756/j.gtxyj.2024.230051.
摘要
【目的】
2
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】
2
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】
2
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2 dB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】
2
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】
2
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】
2
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 numerical solutions of the Raman power coupling equations to improve the accuracy of the FRA output signal gains.
【Results】
2
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
respectively. Additionally
the mean and variance of the maximum error of the gains are 0.462 and 0.044 dB
respectively.
【Conclusion】
2
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
Keywords
FRAneural networknumerical solution of equationsgain
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