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

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

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...

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Hauptverfasser: Srivallapanondh, Sasipim, Freire, Pedro J, Alam, Ashraful, Costa, Nelson, Spinnler, Bernhard, Napoli, Antonio, Sedov, Egor, Turitsyn, Sergei K, Prilepsky, Jaroslaw E
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Sprache:eng
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Computer Science - Learning
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Zusammenfassung: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.
DOI:10.48550/arxiv.2307.05374