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Modulation format and baud-rate identification using asynchronous single channel sampling based on CNN

机译:基于CNN的异步单通道采样调制格式和波特率识别

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摘要

In this paper, an asynchronous single channel sampling (ASCS) based joint modulation format and baud rate identification (MF-BRI) method is proposed for M-QAM optical systems using Convolutional Neural Network (CNN), which significantly reduces the cost and complexity of monitoring system setup. Based on the three influencing factors of OSNR, CD and DGD, three sampling rates are analyzed to generate ASCS images to build the database. The simulation and experiment accuracy of MFI and MF-BRI is above 99.8%. We also discuss the differences between two-channel sampling (TCS) and single-channel sampling (SCS).
机译:在本文中,提出了一种基于异步单通道采样(ASCS)的联合调制格式和波特率识别(MF-BRI)方法,用于使用卷积神经网络(CNN)的M-QAM光学系统,这显着降低了成本和复杂性 监控系统设置。 基于OSNR,CD和DGD的三个影响因素,分析了三个采样率以生成ASCS图像以构建数据库。 MFI和MF-BRI的仿真和实验精度高于99.8%。 我们还讨论了双通道采样(TCS)和单通道采样(SCS)之间的差异。

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