Power evolution prediction of bidirectional Raman amplified WDM system based on PINN.

We propose using physical-informed neural network (PINN) for power evolution prediction in bidirectional Raman amplified WDM systems with Rayleigh backscattering (RBS). Unlike models based on data-driven machine learning, PINN can be effectively trained without preparing a large amount of data in advance and can learn the potential rules of power evolution. Compared to previous applications of PINN in power prediction, our model considers bidirectional Raman pumping and RBS, which is more practical.

Modulation format-independent optical performance monitoring technique insensitive to chromatic dispersion and polarization mode dispersion using a multi-task artificial neural network.

We propose and experimentally demonstrate modulation format-independent optical performance monitoring (OPM) based on a multi-task artificial neural network (MT-ANN). Optical power measurements at a series of center wavelengths adjusted using a widely tunable optical bandpass filter (OBPF) are used as the input features for a MT-ANN to simultaneously realize high-precision optical signal-to-noise ratio (OSNR) and launch power monitoring and baud rate identification (BRI). This technique is insensitive to chromatic dispersion (CD) and polarization mode dispersion (PMD).

Modulation-format-independent in-band OSNR monitoring technique using Gaussian process regression for a Raman amplified multi-span system with a cascaded filtering effect.

We propose and experimentally demonstrate an accurate modulation-format-indepen-dent and cascaded filtering effect (CFE) insensitive in-band optical signal-to-noise ratio (OSNR) monitoring technique enabled by Gaussian process regression (GPR) utilizing a widely tunable optical bandpass filter (OBPF) and optical power measurements. By adjusting the center frequency of a widely tunable OBPF and measuring the corresponding output optical power as the input features of GPR, the proposed OSNR monitoring technique is experimentally proven to be transparent to modulation formats and robust to CFE, chromatic dispersion (CD), polarization mode dispersion (PMD), and nonlinear effect (NLE). Experimental results for 9-channel 32Gbaud PDM-16QAM signals with 50GHz channel spacing demonstrate OSNR monitoring with the root mean squared error (RMSE) of 0.