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arXiv · 1804.07675

Achievable Information Rates for Nonlinear Fiber Communication via End-to-end Autoencoder Learning

Shen Li·Christian Häger·Nil Garcia·Henk Wymeersch
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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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Shen Li, Christian Häger, Nil Garcia, Henk Wymeersch. 2018-09-17. Achievable Information Rates for Nonlinear Fiber Communication via End-to-end Autoencoder Learning. https://arxiv.org/abs/1804.07675

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