Transfer learning simplified multi-task deep neural network for PDM-64QAM optical performance monitoring
Transfer learning simplified multi-task deep neural network for PDM-64QAM optical performance monitoring
复制标题
用于 PDM-64QAM 光学性能监控的迁移学习简化多任务深度神经网络
DOI：
10.1364/oe.388491
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发表时间：
2020-03-02
期刊：
影响因子：
3.8
通讯作者：
Liu, Deming
中科院分区：
文献类型：
--
作者：
Cheng, Yijun;Zhang, Wenkai;Liu, Deming
我们通过实验展示了一种迁移学习（TL）简化的多任务深度神经网络（MT-DNN），用于从直接检测的PDM-64QAM信号中联合进行光信噪比（OSNR）监测和调制格式识别（MFI）。首先，我们研究了振幅直方图（AH）生成质量对OSNR监测性能的影响，并通过实验阐明了更高的电子采样率对于实现高阶QAM格式的精确OSNR监测的重要性。接下来，通过从仿真到实验的TL实现，同时考虑10Gbaud PDM-16QAM和PDM-64QAM信号时，MFI的精度达到100%，OSNR监测的均方根误差（RMSE）为1。PDM-16QAM和PDM-64QAM分别在14-24dB和23-34dB的范围内提高了9db。同时，使用的训练样本和epoch分别大幅减少了24.5%和44.4%。由于采用单个光电探测器（PD）和一个TL简化MT-DNN，因此所提出的光学性能监测（OPM）方案具有较高的性价比，可以应用于高级调制格式。(C) 2020年美国光学学会根据OSA开放获取出版协议的条款
We experimentally demonstrate a transfer learning (TL) simplified multi-task deep neural network (MT-DNN) for joint optical signal-to-noise ratio (OSNR) monitoring and modulation format identification (MFI) from directly detected PDM-64QAM signals. First, we investigate the quality of amplitude histogram (AH) generation on the performance of OSNR monitoring and experimentally clarify the importance of higher electronic sampling rate in order to realize precise OSNR monitoring for high-order QAM format. Next, by implementing TL from simulation to experiment, when both 10Gbaud PDM-16QAM and PDM-64QAM signals are considered, the accuracy of MFI reaches 100% and the root-mean-square error (RMSE) of OSNR monitoring is 1 .09dB over a range of 14-24dB and 23-34dB for PDM-16QAM and PDM-64QAM, respectively. Meanwhile, the used training samples and epochs can be substantially reduced by 24.5% and 44.4%, respectively. Since single photodetector (PD) and one TL simplified MT-DNN are used, the proposed optical performance monitoring (OPM) scheme with high cost performance can be applied for advanced modulation formats. (C) 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement