Abstract: 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 7 200 constellation maps in the simulation dataset show that the proposed model has achieved superior performance compared to existing methods.

Key words: Machine learning, OSNR monitoring, Modulation format classification, Fine-grained image recognition, Residual neural network