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

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. Proposed Autoencoder Structure In machine learning, an AE is a neural network (NN) which consists of two parts: an encoder maps an input s (e.g., an image) to a lowerdimensional representation or code and a decoder attempts to reconstruct the input from the code. The AE structure used in this paper is shown in Figure 1 and will be described in the following. The goal is to transmit a message s chosen from a set of M possible messages {1, 2,..., M} M. Following 9 In this paper, we develop an AE for a simplified memoryless fiber channel model.