Out-of-Field Generic ML Training with In-Field Specific Adaptation to Facilitate ML Deployments

A two-phase strategy to facilitate ML algorithm deployment in real networks is demonstrated: out-of-field training uses data from simulation and testbed experiments with generic equipment whereas in-field adaptation is applied to support heterogeneous equipment.