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arXiv · 2307.05374

Multi-Task Learning to Enhance Generalizability of Neural Network Equalizers in Coherent Optical Systems

Sasipim Srivallapanondh·Pedro J. Freire·Ashraful Alam·Nelson Costa·Bernhard Spinnler·Antonio Napoli·Egor Sedov·Sergei K. Turitsyn·Jaroslaw E. Prilepsky
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Abstract

For the first time, multi-task learning is proposed to improve the flexibility of NN-based equalizers in coherent systems. A "single" NN-based equalizer improves Q-factor by up to 4 dB compared to CDC, without re-training, even with variations in launch power, symbol rate, or transmission distance.

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Sasipim Srivallapanondh, Pedro J. Freire, Ashraful Alam, Nelson Costa, Bernhard Spinnler, Antonio Napoli, Egor Sedov, Sergei K. Turitsyn, Jaroslaw E. Prilepsky. 2023-07-04. Multi-Task Learning to Enhance Generalizability of Neural Network Equalizers in Coherent Optical Systems. https://arxiv.org/abs/2307.05374

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