基于信号功率非线性变换, 结合神经网络, 文章提出了一种利用深度神经网络(DNN)实现光信噪比(OSNR)监测的方法。通过对信号功率、信号2次方、4次方和8次方运算后对应的快速傅里叶变换(FFT)后的幅度获取信号与OSNR相关特征量, 并利用DNN提取相关特征量以实现OSNR监测。仿真结果表明, 针对28 Gbaud 偏振复用(PDM)正交相移键控(QPSK)、PDM-8相移键控(PSK)、PDM-8正交振幅调制(QAM)和PDM-16QAM信号相干光通信系统, 对应的背靠背OSNR监测平均标准误差分别为0.10、0.09、0.33和0.46 dB。对这4种调制格式, 在入纤功率分别为4、4、3和3 dBm, 传输距离分别为2 000、1 040、1 040和800 km单模光纤时, 获得的OSNR监测平均标准误差分别为0.43、0.34、0.66和0.79 dB。

This paper proposes and demonstrates an Optical Signal Noise Ratio (OSNR) monitoring scheme using the signal power nonlinear transformation and Deep Neural Networks (DNN). The features of signal after 2th, 4th, and 8th transformation and corresponding Fast Fourier Transformation (FFT) depend on the OSNR of signal. By utilizing the DNN to extract those OSNR depended specific features, the OSNR value can be estimated. Simulation results for 28 Gbaud Polarization Division Multiplexing (PDM)-Quadrature Phase Shift Keying (QPSK), PDM-8 Phase Shift Keying (PSK), PDM-8 Quadrature Amplitude Modulation (QAM) and PDM-16QAM signals show that the OSNR monitoring with mean estimation standard errors of 0.10, 0.09, 0.33 and 0.46 dB in back-to-back case and 0.43, 0.34, 0.66 and 0.79 dB in 2 000, 1 040, 1 040 and 800 km single mode fiber transmission case with input optical power of 4, 4, 3 and 3 dBm, respectively.