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

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Publication date: 20 April 2018

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.

Has companion code repository: https://github.com/henkwymeersch/AutoencoderFiber

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