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Model-Based Machine Learning for Joint Digital Backpropagation and PMD Compensation

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Abstract

We propose a model-based machine-learning approach for polarization-multiplexed systems by parameterizing the split-step method for the Manakov-PMD equation. This approach performs hardware-friendly DBP and distributed PMD compensation with performance close to the PMD-free case.

© 2020 The Author(s)

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