Domain Adaptation: the Key Enabler of Neural Network Equalizers in Coherent Optical Systems

• URL: http://arxiv.org/abs/2202.12689v1
• Date: Fri, 25 Feb 2022 13:46:33 GMT
• Title: Domain Adaptation: the Key Enabler of Neural Network Equalizers in Coherent Optical Systems
• Authors: Pedro J. Freire, Bernhard Spinnler, Daniel Abode, Jaroslaw E. Prilepsky, Abdallah A. I. Ali, Nelson Costa, Wolfgang Schairer, Antonio Napoli, Andrew D. Ellis, Sergei K. Turitsyn
• Abstract summary: We introduce the domain adaptation and randomization approach for calibrating neural network-based equalizers for real transmissions. The approach renders up to 99% training process reduction.
• Score: 1.4549914190846531
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce the domain adaptation and randomization approach for calibrating neural network-based equalizers for real transmissions, using synthetic data. The approach renders up to 99\% training process reduction, which we demonstrate in three experimental setups.

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