相量
非线性系统
反向
光学
傅里叶变换
摄动(天文学)
快速傅里叶变换
计算机科学
单模光纤
光纤
算法
控制理论(社会学)
物理
数学
数学分析
人工智能
功率(物理)
几何学
电力系统
量子力学
控制(管理)
作者
Hubert Dzieciol,Toshiaki Koike-Akino,Ye Wang,Kieran Parsons
摘要
We improve an inverse regular perturbation (RP) model using a machine learning (ML) technique. The proposed learned RP (LRP) model jointly optimizes step-size, gain and phase rotation for individual RP branches. We demonstrate that the proposed LRP can outperform the corresponding learned digital back-propagation (DBP) method based on a split-step Fourier method (SSFM), with up to 0.75 dB gain in a 800 km standard single mode fiber link. Our LRP also allows a fractional step-per-span (SPS) modeling to reduce complexity while maintaining superior performance over a 1-SPS SSFM-DBP.
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