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.

214.7 Tbit/s S, C, and L-band transmission over 50 km SSMF based on silicon photonic integrated transceivers.

We experimentally demonstrate a 214.7 Tbit/s generalized mutual information (GMI) estimated throughput by ultra-wideband wavelength division multiplexing (WDM) transmission in standard single-mode fiber (SSMF). With 50-GHz grid, 396 transmission channels are used to deliver 49 GBaud probabilistically constellation-shaped (PCS) 256 quadrature amplitude modulation (QAM) and PCS-64QAM signals.