We propose a less complex neural network that estimates and equalizes the nonlinear distortion of single frequency dual polarization data transmitted through a single mode optical fiber. We then analyze the influence of the size of the input data symbol window on the neural network design and the enhancement of the quality factor (Q-factor) that can be achieved by integrating the neural network with a perturbative nonlinearity compensation model. We significantly reduce the complexity of the neural network by determining the most significant inputs for the neural network from the self-phase modulation terms (intra-cross phase modulation and intra-four wave mixing) in the model. The weight matrices of the neural network are determined without prior knowledge of the system parameters while the complexity of the network is reduced in two stages through weight trimming technique and principle component analysis (PCA). Applying our procedure to a 3200 km double polarization 16-QAM optical system yields a ≈ 0.85 dB Q-factor enhancement with a 35% smaller number of inputs compared to previous designs.

中文翻译：

---

基于扰动的神经网络接收器的非线性缓解

我们提出了一种不太复杂的神经网络，用于估计和均衡通过单模光纤传输的单频双偏振数据的非线性失真。然后我们分析了输入数据符号窗口的大小对神经网络设计的影响以及通过将神经网络与微扰非线性补偿模型集成可以实现的品质因子（Q-factor）的增强。我们通过从模型中的自相位调制项（交叉内相位调制和四波内混合）确定神经网络的最重要输入，显着降低了神经网络的复杂性。神经网络的权矩阵是在没有系统参数的先验知识的情况下确定的，而网络的复杂性通过权重修剪技术和主成分分析（PCA）分两个阶段降低。将我们的程序应用于 3200 公里双偏振 16-QAM 光学系统，与以前的设计相比，输入数量减少了 35%，产生了 ≈ 0.85 dB 的 Q 因子增强。