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Conferences > OFC > 2020 > W3D > W3D.4

End-to-End Learning of Geometrical Shaping Maximizing Generalized Mutual Information

Kadir Gümüş, Alex Alvarado, Bin Chen, Christian Häger, and Erik Agrell

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Abstract

GMI-based end-to-end learning is shown to be highly nonconvex. We apply gradient descent initialized with Gray-labeled APSK constellations directly to the constellation coordinates. State-of-the-art constellations in 2D and 4D are found providing reach increases up to 26% w.r.t. to QAM.

© 2020 The Author(s)

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