Investigating the Performance and Suitability of Neural Network Architectures for Nonlinearity Mitigation of Optical Signals

Vegenshanti Dsilva; Isaac Sackey; Gregor Ronniger; Guillermo von Hünefeld; Guillermo von Hünefeld; Binoy Chacko; Colja Schubert; Ronald Freund; Ronald Freund

European Conference on Optical Communication (ECOC) 2022
Technical Digest Series (Optica Publishing Group, 2022),
paper Tu5.27

Investigating the Performance and Suitability of Neural Network Architectures for Nonlinearity Mitigation of Optical Signals

Vegenshanti Dsilva, Isaac Sackey, Gregor Ronniger, Guillermo von Hünefeld, Binoy Chacko, Colja Schubert, and Ronald Freund

European Conference and Exhibition on Optical Communication 2022

Basel Switzerland
18–22 September 2022
ISBN: 978-1-957171-15-9

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Joint Poster Session I (Tu5)

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V. Dsilva, I. Sackey, G. Ronniger, G. von Hünefeld, B. Chacko, C. Schubert, and R. Freund, "Investigating the Performance and Suitability of Neural Network Architectures for Nonlinearity Mitigation of Optical Signals," in European Conference on Optical Communication (ECOC) 2022, J. Leuthold, C. Harder, B. Offrein, and H. Limberger, eds., Technical Digest Series (Optica Publishing Group, 2022), paper Tu5.27.

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Abstract

We compare three different neural network architectures for nonlinearity mitigation of 32 GBd OOK and QPSK signals after transmission over a dispersion-compensated link of 10-km SSMF and 10-km DCF. OSNR gains up to 2.2 dB were achieved using reservoir networks, suitable for fast training.

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