论文标题
使用机器学习的拉曼放大器的同时增益概况设计和噪声图预测
Simultaneous gain profile design and noise figure prediction for Raman amplifiers using machine learning
论文作者
论文摘要
实验证明了一个机器学习框架，可预测目标分布式拉曼放大器增益曲线的泵功率和噪声图。我们采用一个单层神经网络来学习从增益曲线到泵功能和噪声数字的映射。获得的结果显示出高度准确的增益曲线设计和噪声图预测，平均最大误差约为0.3dB。该框架提供了拉曼放大器的全面表征，因此是预测下一代光学通信系统的性能的有价值的工具，该工具预计将采用拉曼放大。
A machine learning framework predicting pump powers and noise figure profile for a target distributed Raman amplifier gain profile is experimentally demonstrated. We employ a single-layer neural network to learn the mapping from the gain profiles to the pump powers and noise figures. The obtained results show highly-accurate gain profile designs and noise figure predictions, with a maximum error on average of ~0.3dB. This framework provides the comprehensive characterization of the Raman amplifier and thus is a valuable tool for predicting the performance of the next-generation optical communication systems, expected to employ Raman amplification.
