Knowledge distillation aided by probability information enabled efficient joint monitoring of SNRL and SNRNL for WDM systems.

For wavelength division multiplexing (WDM) systems, excessive linear and nonlinear noise will seriously decrease the quality of optical signals, and the effective joint monitoring scheme can prevent the degradation of system performance due to noise accumulation. In this paper, we propose a probability information assisted knowledge distillation (PIAKD) scheme that achieves intelligent joint monitoring for linear signal-to-noise ratio (SNRL) and nonlinear signal-to-noise ratio (SNRNL) in WDM systems. Under the condition of multi-task regression, outputs are independent and continuous, PIAKD addresses the longstanding challenge that the student model fails to effectively learn knowledge from the teacher model by introducing probability information into the loss function.

Joint intra and inter-channel nonlinear compensation scheme based on improved learned digital back propagation for WDM systems.

In this paper, we improve the learned digital back propagation (LDBP) and propose a novel joint intra and inter-channel nonlinearity compensation scheme for polarization division multiplexing wavelength-division multiplexed (PDM-WDM) systems. From the perspective of interpretable neural network, the scheme realizes the alternating compensation of chromatic dispersion (CD) and nonlinearity based on physical models. The chromatic dispersion compensation (CDC) adopts one-dimensional convolution operation in the time domain.