基于CNN-LSTM和海马优化算法的二阶拉曼光纤放大器设计方案  

Second-order Raman fiber amplifier design scheme based on CNN-LSTM and sea horse optimization algorithm

作　　者：蒋杰伟 金库 朱少民 刘尚辉 巩稼民 JIANG Jiewei;JIN Ku;ZHU Shaomin;LIU Shanghui;GONG Jiamin(School of Electronic Engineering,Xi′an University of Posts and Telecommunications,Xi'an,Shaanxi 710121,China;School of Communication and Information Engineering,Xi′an University of Posts and Telecommunications,Xi'an,Shaanxi 710121,China)

机构地区：[1]西安邮电大学电子工程学院,陕西西安710121 [2]西安邮电大学通信与信息工程学院,陕西西安710121

出　　处：《光电子．激光》2024年第10期1009-1017,共9页Journal of Optoelectronics·Laser

基　　金：国家自然科学基金(61775180,62276210);陕西省自然科学基础研究计划(2022JM-380);陕西省西安市大学研究生创新基金项目(CXJJDL2022009)资助项目。

摘　　要：随着通信系统向高速率、超带宽不断发展,适应这种发展的高性能拉曼放大器的设计逐渐成为研究重点。然而,由于输出拉曼增益、噪声和泵浦参数之间复杂的非线性关系,设计高性能的拉曼放大器具有挑战性。传统的数值优化方法在解决这个问题上效率不佳。为了解决这个问题,本文提出了一个使用卷积神经网络(convolutional neural network,CNN)和长短期记忆网络(long-short term memory,LSTM)的二阶拉曼光纤放大器(Raman fiber amplifier,RFA)增益和噪声预测模型。研究了不同预测模型性能对设计拉曼光纤放大器的影响,并利用海马算法优化模型,以准确反映泵浦参数、光纤长度和目标增益和噪声分布之间的映射关系。实验结果表明,本文提出的模型在增益和噪声预测方面的均方根误差分别只有0.0431和0.0224 dB,预测值和目标值之间的误差小于0.25 dB,平均耗时小于0.1337 s。该设计方案为未来RFA的快速设计提供了方法和思路。With the continuous advancement of communication systems towards high-speed and ultra-wideband,there has been a growing research focus on designing high-performance Raman amplifiers tailored to this progress.However,designing high-performance Raman amplifiers is challenging due to the complex non-linear relationship between output Raman gain,noise,and pump parameters.Traditional numerical optimization methods are not efficient in solving this problem.To address this,this paper proposes a second-order Raman fiber amplifier(RFA)gain and noise prediction model using convolutional neural network(CNN)and long-short term memory(LSTM).The impact of different prediction model performances on the design of Raman fiber amplifiers is investigated,and to optimize the model using the sea horse optimizer(SHO)algorithm to accurately reflect the mapping relationship between pump parameters,fiber length,and the target gain and noise distribution.Experimental results show that the proposed model has a root mean square error of only 0.0431 and 0.0224 dB in gain and noise prediction,with an error between the predicted and target values below 0.25 dB and an average consumption time of less than 0.1337 s.This design provides methods and ideas for the rapid design of Raman fiber amplifiers in the future.

关 键 词：二阶拉曼光纤放大器(RFA) 卷积神经网络(CNN) 长短期记忆网络(LSTM) 海马优化(SHO)算法 拉曼增益 

分 类 号：TN929.11[电子电信—通信与信息系统]