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首页> 外文期刊>Journal of Lightwave Technology >Advanced Convolutional Neural Networks for Nonlinearity Mitigation in Long-Haul WDM Transmission Systems
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Advanced Convolutional Neural Networks for Nonlinearity Mitigation in Long-Haul WDM Transmission Systems

机译：长途WDM传输系统中的高级卷积神经网络

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摘要

Practical implementation of digital signal processing for mitigation of transmission impairments in optical communication systems requires reduction of the complexity of the underlying algorithms. Here, we investigate the application of convolutional neural networks for compensating nonlinear signal distortions in a 3200 km fiber-optic 11x400-Gb/s WDM PDM-16QAM transmission link with a focus on the optimization of the corresponding algorithmic complexity. We propose a design that includes original initialisation of the weights of the layers by a filter predefined through the training a single-layer convolutional neural network. Furthermore, we use an enhanced activation function that takes into account nonlinear interactions between neighbouring symbols. To increase learning efficiency, we apply a layer-wise training scheme followed by joint optimization of all weights applying additional training to all of them together in the large multi-layer network. We examine application of the proposed convolutional neural network for the nonlinearity compensation using only one sample per symbol and evaluate complexity and performance of the proposed technique.
机译：用于减轻光学通信系统中传动障碍的减轻的数字信号处理的实际实现需要降低底层算法的复杂性。在这里，我们调查卷积神经网络在3200km光纤11x400-GB / S WDM PDM-16QAM传输链路中补偿非线性信号失真的应用，重点是对应算法复杂性的优化。我们提出了一种设计，该设计包括通过训练单层卷积神经网络预定义的层的原始初始化。此外，我们使用增强的激活函数，该激活函数考虑了相邻符号之间的非线性交互。为了提高学习效率，我们应用了一层明智的培训方案，然后在大型多层网络中将额外训练应用于所有重量的所有权重。我们使用每个符号的一个样品研究了拟议的卷积神经网络的应用，并评估了所提出的技术的复杂性和性能。

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