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End-to-End Learning of Geometrical Shaping Maximizing Generalized Mutual Information
December 11, 2019 Β· Declared Dead Β· π Optical Fiber Communications Conference and Exhibition
"No code URL or promise found in abstract"
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Authors
Kadir GΓΌmΓΌs, Alex Alvarado, Bin Chen, Christian HΓ€ger, Erik Agrell
arXiv ID
1912.05638
Category
eess.SP: Signal Processing
Cross-listed
cs.AI,
cs.IT,
stat.ML
Citations
54
Venue
Optical Fiber Communications Conference and Exhibition
Last Checked
5 months ago
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.
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