For the first time, recurrent and feedforward neural network-based equalizers for nonlinearity compensation are implemented in an FPGA, with a level of complexity comparable to that of a dispersion equalizer. We demonstrate that the NN-based equalizers can outperform a 1 step-per-span DBP.

翻译：以神经网络为基础的非线性补偿经常和饲料平衡器首次在FPGA中实施,其复杂程度与分散平衡器的复杂程度相当。 我们证明,以NN为基地的平衡器的性能可以超过每步一步的DBP。