Power and Modulation Format Transfer Learning for Neural Network Equalizers in Coherent Optical Transmission Systems

Daniel Abode; Jaroslaw E. Prilepsky; Pedro J. Freire; Sergei K. Turitsyn

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Power and Modulation Format Transfer Learning for Neural Network Equalizers in Coherent Optical Transmission Systems

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arxiv 2106.13144 v1 pith:VUENCO3I submitted 2021-06-24 eess.SP

Pedro J. Freire , Daniel Abode , Jaroslaw E. Prilepsky , Sergei K. Turitsyn This is my paper · ORCID

classification eess.SP

keywords learningmodulationtransfertransmissionacrossadaptcoherentdataset

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Transfer learning is proposed to adapt an NN-based nonlinear equalizer across different launch powers and modulation formats using a 450km TWC-fiber transmission. The result shows up to 92% reduction in epochs or 90% in the training dataset.

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