We propose a modification of the conventional perturbation-based approach of fiber nonlinearity compensation that enables straight-forward implementation at the receiver and meets feasible complexity requirements. We have developed a model based on perturbation analysis of an inverse Manakov problem, where we use the received signal as the initial condition and solve Manakov equations in the reversed direction, effectively implementing a perturbative digital backward propagation enhanced by machine learning techniques. To determine model coefficients we employ machine learning methods using a training set of transmitted symbols. The proposed approach allowed us to achieve 0.5 dB and 0.2 dB $Q^2$-factor improvement for 2000 km transmission of 11 × 256 Gbit/s DP-16QAM signal compared to chromatic dispersion equalization and one step per span two samples per symbol digital back-propagation technique, respectively. We quantify the trade-off between performance and complexity.

中文翻译：

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使用复杂度降低的逆扰动理论补偿非线性损伤

我们建议对传统的基于扰动的光纤非线性补偿方法进行修改，该方法可以在接收器处直接实现并满足可行的复杂性要求。我们开发了一个基于逆 Manakov 问题微扰分析的模型，其中我们使用接收信号作为初始条件并在相反方向求解 Manakov 方程，有效地实现了通过机器学习技术增强的微扰数字反向传播。为了确定模型系数，我们使用机器学习方法使用传输符号的训练集。建议的方法使我们能够达到 0.5 dB 和 0。与色散均衡相比，11 × 256 Gbit/s DP-16QAM 信号的 2000 公里传输的 2 dB $Q^2$ 因子改进分别为每跨步两个样本每个符号数字反向传播技术。我们量化了性能和复杂性之间的权衡。