Convolutional-Neural-Network-Based Fiber Optic Channel Emulator and Its Application to Fiber-Longitudinal Power Profile Estimation

This paper proposes an accuracy enhancement method for the fiber-longitudinal power profile estimation (PPE) based on convolutional neural networks (CNN). Two types of CNNs are designed. The first one is a network that treats different polarization streams identically. This network is denoted as CNN. The second one considers the difference between the contributions of different polarization streams to the nonlinear phase shift and is denoted as enhanced CNN (ECNN). The numerical simulation results confirm the effectiveness of the method for a 64 GBaud/s DP-QPSK system with a fiber length of 320 km. The effects of finite impulse response filter (FIR) length, power into the fiber, and polarization mode dispersion on the PPE accuracy are examined. Finally, monitoring results of the proposed method in the presence of several simultaneous power attenuation anomalies in the fiber optic link are shown. It is found that the accuracy of the PPE substantially improves after using the proposed method, achieving a relative gain of up to 71%.