基于卷积神经网络的光信噪比监测方法

Optical signal-to-noise ratio monitoring method based on convolutional neural network

光信噪比(OSNR)与光纤通信的传输性能息息相关,因此,OSNR监测是光性能监测技术中至关重要的一环,同时,传输链路中的色散会导致光信号脉冲展宽,使OSNR监测准确性下降。针对这一问题,设计了一种卷积神经网络模型,以异步延迟采样图(ADTP)作为网络输入特征,引入实例批量标准化模块,继承了神经网络不同深度下特征发散分布的优点,提高了神经网络对色散变化的适应性。实验结果表明,在10 Gb/s NRZ-OOK信号无色散干扰监测场景下,该模型的平均绝对误差(MAE)为0.2 dB,在色散变化的场景下,MAE最高降低了0.61 dB。

Optical signal-to-noise ratio(OSNR)is closely related to the transmission performance of optical fiber communication,so OSNR monitoring is a crucial part of optical performance monitoring technology.At the same time,the dispersion in the transmission link will lead to the broadening of optical signal pulses,which will reduce the accuracy of OSNR monitoring.Aiming at this problem,a convolutional neural network model is designed.The asynchronous delay sampling graph(ADTP)is used as the network input feature,and the instance batch standardization module is introduced.It inherits the advantages of feature divergence distribution at different depths of the neural network and improves the adaptability of the neural network to dispersion changes.The experimental results show that the mean absolute error(MAE)of the model is 0.2 dB in the case of 10 Gb/s NRZ-OOK signal without dispersion interference monitoring,and the MAE is reduced by 0.61 dB at most in the case of dispersion change.