Identifying modulation formats through 2D Stokes planes with deep neural networks.
Identifying modulation formats through 2D Stokes planes with deep neural networks.
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DOI：
10.1364/oe.26.023507
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发表时间：
2018-08
期刊：
影响因子：
3.8
通讯作者：
Wenbo Zhang;Dingcheng Zhu;Zihang He;Nannan Zhang;Xiaoguang Zhang;Hu Zhang;Yong Li
中科院分区：
文献类型：
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作者：
Wenbo Zhang;Dingcheng Zhu;Zihang He;Nannan Zhang;Xiaoguang Zhang;Hu Zhang;Yong Li
提出并演示了一种用于偏振域复用（PDM）光纤通信系统的基于轻量级卷积（深度）神经网络（CNN）的二维斯托克斯平面调制格式识别（MFI）方案。讨论了CNN的学习速率对算法性能的影响。在28 GBaud的符号速率下对PDM系统进行了实验验证。六个调制格式识别与训练的CNN从接收信号的图像。它们是PDM-BPSK、PDM-QPSK、PDM-8 PSK、PDM-16 QAM、PDM-32 QAM和PDM-64 QAM。通过利用计算机视觉，结果表明，该方案可以显着提高识别性能比现有技术。
A lightweight convolutional (deep) neural networks (CNNs) based modulation format identification (MFI) scheme in 2D Stokes planes for polarization domain multiplexing (PDM) fiber communication system is proposed and demonstrated. Influences of the learning rate of CNN is discussed. Experimental verifications are performed for the PDM system at a symbol rate of 28GBaud. Six modulation formats are identified with a trained CNN from images of received signals. They are PDM-BPSK, PDM-QPSK, PDM-8PSK, PDM-16QAM, PDM-32QAM, and PDM-64QAM. By taking advantage of computer vision, the results show that the proposed scheme can significantly improve the identification performance over the existing techniques.