Improving QoT estimation accuracy with DGE monitoring using machine learning

Abstract

In optical transport networks, Dynamic Gain Equalizers (DGE) are typically used at each link. A DGE selectively attenuates the channels to compensate the cumulative Erbium Doped Fiber Amplifier (EDFA) gain ripple effect on the multi-span link, resulting in almost flat output power at the end of the link. We leverage monitored per link DGE attenuation profiles and coherent receivers Signal to Noise Ratio (SNR) information, and propose a machine learning (ML) based scheme to estimate the EDFA gain ripple penalties for new connections. Using that in realistic simulation scenarios we observed a design margin reduction from ~1dB to ~0.3dBs.