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Deep Learning of Geometric Constellation Shaping including Fiber Nonlinearities (1805.03785v1)
Published 10 May 2018 in cs.IT, math.IT, and stat.ML
Abstract: A new geometric shaping method is proposed, leveraging unsupervised machine learning to optimize the constellation design. The learned constellation mitigates nonlinear effects with gains up to 0.13 bit/4D when trained with a simplified fiber channel model.