Demonstration of Automated ML-Driven Energy-Efficient Spectrum Defragmentation for Optical Transport Networks

We introduce an ML-driven framework for energy-efficient spectrum defragmentation, automatically forecasting daily traffic, setting optical channels to standby during low-demand, and reallocating spectrum to reduce fragmentation while reactivating optical channels as necessary.