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

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Abstract

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.

© 2019 The Author(s)

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