Dynamic power pre-adjustments with machine learning that mitigate EDFA excursions during defragmentation

We examine EDFA power excursions during three defragmentation methods of flexgrid super-channels. Using a machine learning approach, we demonstrate automatic and dynamic adjustments of pre-EDFA power levels, and show the mitigation of post-EDFA power discrepancy among channels by over 62%.