Multi-Task Learning to Enhance Generalizability of Neural Network Equalizers in Coherent Optical Sys...
Multi-Task Learning to Enhance Generalizability of Neural Network Equalizers in Coherent Optical Systems
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Ithaca: Cornell University Library, arXiv.org
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English
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Ithaca: Cornell University Library, arXiv.org
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Contents
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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Multi-Task Learning to Enhance Generalizability of Neural Network Equalizers in Coherent Optical Systems
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TN_cdi_proquest_journals_2836087223
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2836087223
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E-ISSN
2331-8422
