论文标题
数据驱动的时间域一阶常规扰动模型的增强
Data-driven Enhancement of the Time-domain First-order Regular Perturbation Model
论文作者
论文摘要
提出了标准化的批次下降优化器，以改善Manakov方程的一阶常规扰动系数，通常称为内核。优化基于一阶常规扰动提供的线性参数化，并针对光纤通道增强了低复杂模型。我们证明，优化的模型优于分析对应物，其中通过其积分形式对内核进行数字评估。增强型模型在覆盖非线性和高度非线性方案的扩展功率范围内运行时，核数量减少，可提供相同的精度。根据所使用的度量，获得$ 6-7 $ 〜db的增益，相对于常规的一阶常规扰动获得。
A normalized batch gradient descent optimizer is proposed to improve the first-order regular perturbation coefficients of the Manakov equation, often referred to as kernels. The optimization is based on the linear parameterization offered by the first-order regular perturbation and targets enhanced low-complexity models for the fiber channel. We demonstrate that the optimized model outperforms the analytical counterpart where the kernels are numerically evaluated via their integral form. The enhanced model provides the same accuracy with a reduced number of kernels while operating over an extended power range covering both the nonlinear and highly nonlinear regimes. A $6-7$~dB gain, depending on the metric used, is obtained with respect to the conventional first-order regular perturbation.
