.png){kind=icon}
返回
Fiber-Agnostic Machine Learning-Based Raman Amplifier Models
.png){kind=icon}
OA
AI
DOI：10.1109/JLT.2022.3210769.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, we show that by combining experimental data from different optical fibers, we can build a fiber-agnostic neural-network to model the Raman amplifier. The fiber-agnostic NN model can predict the gain profile of a new fiber type (unseen by the model during training) with a maximum absolute error as low as 0.22 dB. We show that this generalization is only possible when the unseen fiber parameters are similar to the fibers used to build the model. Therefore, a training dataset with a wide range of optical fibers parameters is needed to enhance the chance of accurately predicting the gain of a new fiber. This implies that time-consuming experimental measurements of various fiber types can be avoided. For that, here we extend and improve our general model by numerically generating the dataset. By doing so, it is possible to generate uniformly distributed data that covers a wide range of optical fiber types. The results show that the averaged maximum prediction error is reduced when compared to the limited experimental data-based general models. As the second and final contribution of this work, we propose the use of transfer learning (TL) to re-train the numerical data-based general model using just a few experimental measurements. Compared with the fiber-specific models, this TL-upgraded general model reaches very similar accuracy, with just 3.6% of the experimental data. These results demonstrate that the already fast and accurate NN-based RA models can be upgraded to have strong fiber generalization capabilities.
Keyword:
Optical communications
optical amplifiers
machine learning
neural networks
期刊
IF:
4.8
论文数:
1.7W
被引数:
3.8W
机构
引用论文
Modeling EDFA Gain Ripple and Filter Penalties With Machine Learning for Accurate QoT Estimation
OAAI
OAAI分享收藏Experimental Characterization of Raman Amplifier Optimization Through Inverse System Design通过逆系统设计优化拉曼放大器的实验表征OAAI
OAAI分享收藏分享收藏Introducing Load Aware Neural Networks for Accurate Predictions of Raman Amplifiers引入负载感知神经网络以准确预测拉曼放大器OAAI
OAAI分享收藏Study of ion dynamics in lanthanum aluminate probed by conductivity spectroscopy.png)
RSC Advances
RSC AdvancesIF0
PREAI
分享收藏Enhancing Lightpath QoT Computation With Machine Learning in Partially Disaggregated Optical Networks在部分分解的光网络中通过机器学习增强光路QoT计算OAAI
OAAI分享收藏
查看更多内容