论文标题
基于模型的机器学习，用于联合数字反向传播和PMD补偿
Model-Based Machine Learning for Joint Digital Backpropagation and PMD Compensation
论文作者
论文摘要
我们通过参数化Manakov-PMD方程的拆分方法来提出一种基于模型的机器学习方法。此方法执行硬件友好的DBP并分配PMD补偿，并且靠近无PMD案例的性能。
We propose a model-based machine-learning approach for polarization-multiplexed systems by parameterizing the split-step method for the Manakov-PMD equation. This approach performs hardware-friendly DBP and distributed PMD compensation with performance close to the PMD-free case.
