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

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.