Zhang Junfeng, Chen Wei, Gao Mingyi, Ma Yuanyuan, Zhao Yongli, Chen Wei, Shen Gangxiang
Opt Express. 2018 Jul 9;26(14):18684-18698. doi: 10.1364/OE.26.018684.
In a cognitive, heterogeneous, optical network, it would be important to identify physical layer information, especially the modulation formats of transmitted signals. The modulation format information is also indispensable for carrier-phase-recovery in a coherent optical receiver. Because constellation diagrams of modulation signals are susceptible to various noises, we utilize a convolutional neural network to process the amplitude data after the modulation-format-agnostic clock recovery. Furthermore, for the carrier-phase-recovered data, we use the clustering method based on a fast search and find the density peaks to classify the constellation clusters and use the k-nearest-neighbor method to label the samples. The proposed receiver system has a simple architecture to identify the modulation format based on the amplitude information and can track fast changes of the signals to improve the accuracy of the symbol decision. We have demonstrated this experimentally and have achieved remarkable BER improvement.
在认知的、异构的光网络中，识别物理层信息，尤其是传输信号的调制格式非常重要。调制格式信息对于相干光接收机中的载波相位恢复也是必不可少的。由于调制信号的星座图容易受到各种噪声的影响，我们利用卷积神经网络在与调制格式无关的时钟恢复后处理幅度数据。此外，对于载波相位恢复后的数据，我们使用基于快速搜索的聚类方法并找到密度峰值来对星座簇进行分类，并使用k近邻方法对样本进行标记。所提出的接收机系统具有简单的架构，可基于幅度信息识别调制格式，并且可以跟踪信号的快速变化以提高符号判决的准确性。我们已经通过实验证明了这一点，并在误码率方面取得了显著改善。
