Transfer Learning for Neural Networks-based Equalizers in Coherent Optical Systems
Pedro J. Freire,
Daniel Abode,
Jaroslaw E. Prilepsky
et al.
Abstract:In this work, we address the paramount question of generalizability and adaptability of artificial neural networks (NNs) used for impairment mitigation in optical transmission systems. We demonstrate that by using well-developed techniques based on the concept of transfer learning, we can efficaciously retrain NN-based equalizers to adapt to changes in the transmission system using just a fraction of the initial training data and resources. We evaluate the potential of transfer learning to adapt the NN to chan… Show more
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