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End-to-End Deep Learning of Long-Haul Coherent Optical Fiber Communications via Regular Perturbation Model (2107.12320v1)
Published 26 Jul 2021 in eess.SP, cs.IT, cs.LG, and math.IT
Abstract: We present a novel end-to-end autoencoder-based learning for coherent optical communications using a "parallelizable" perturbative channel model. We jointly optimized constellation shaping and nonlinear pre-emphasis achieving mutual information gain of 0.18 bits/sym./pol. simulating 64 GBd dual-polarization single-channel transmission over 30x80 km G.652 SMF link with EDFAs.