基于细粒度星座图识别的光性能监测方法

Optical Performance Monitoring Method Based on Fine-grained Constellation Diagram Recognition

在光纤通信中,传统光性能监测(Optical Performance Monitoring,OPM)主要依靠分析信号的时频域信息来实现,但此类方法无法完成多任务联合监测,因此其灵活性较低。随着机器学习的发展,基于机器学习的光信号调制格式(ModulationFormat,MF)及光信噪比(Optical Signal Noise Ratio,OSNR)监测方法被逐渐应用。但现有方法未考虑信号的细粒度特征,因此在复杂场景中对OSNR的监测精度较低。针对上述问题,文中提出了一种基于细粒度星座图识别的光信号MF和OSNR联合监测模型(Fine-Grained Optical Performance Monitor Network,FGNet)。首先,在骨干特征提取模块中采用深度残差结构对星座图进行深度特征提取;其次,提出多层双线性池化(Multilayer Bilinear Pooling)模块,对星座图特征进行细粒度特征分析;最后,提出联合监测模块对MF和OSNR进行特征融合分析。在拥有7200张星座图的仿真数据集中进行广泛的实验,实验结果表明,所提方法相比现有方法取得了更优越的性能。

In optic fiber communication,traditional optical performance monitoring(OPM)mainly relies on analyzing the time-frequency domain information of the signal.However,conventional methods cannot complete multi-task joint monitoring,so they are less flexible.With the development of machine learning,the monitoring of optical signal modulation format(MF)and optical signal-to-noise ratio(OSNR)based on machine learning have been gradually applied.However,existing methods have low accuracy for OSNR monitoring in complex scenarios because they do not consider the fine-grained characteristics of the signal.This paper proposes a joint monitoring model(FGNet)for optical signal MF and OSNR based on fine-grained constellation identification to solve this problem.Firstly,the backbone feature extraction module uses a deep residual structure.Secondly,a multilayer bilinear pooling module is proposed to perform fine-grained feature analysis on constellation features.Finally,a joint MF and OSNR monitoring module is proposed to realize the feature fusion of MF and OSNR.Extensive experiments with 7200 constellation maps in the simulation dataset show that the proposed model has achieved superior performance compared to existing methods.