Abstract

Nonlinearities in SOA-based wavelength conversion are experimentally tackled using post-blind-compensation based on machine learning clustering for coherent 16/64-QAM signals. We show that K-means outperforms fuzzy-logic since it tackles more effectively non-circularly- symmetric Gaussian noise and nonlinearity.

© 2019 The Author(s)

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