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Achievable Information Rates for Nonlinear Fiber Communication via End-to-end Autoencoder Learning
April 20, 2018 Β· Declared Dead Β· π European Conference on Optical Communication
"No code URL or promise found in abstract"
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Authors
Shen Li, Christian HΓ€ger, Nil Garcia, Henk Wymeersch
arXiv ID
1804.07675
Category
cs.IT: Information Theory
Cross-listed
stat.ML
Citations
75
Venue
European Conference on Optical Communication
Last Checked
5 months ago
Abstract
Machine learning is used to compute achievable information rates (AIRs) for a simplified fiber channel. The approach jointly optimizes the input distribution (constellation shaping) and the auxiliary channel distribution to compute AIRs without explicit channel knowledge in an end-to-end fashion.
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