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arXiv · 2107.12320

End-to-End Deep Learning of Long-Haul Coherent Optical Fiber Communications via Regular Perturbation Model

Vladislav Neskorniuk·Andrea Carnio·Vinod Bajaj·Domenico Marsella·Sergei K. Turitsyn·Jaroslaw E. Prilepsky·Vahid Aref
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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.

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Vladislav Neskorniuk, Andrea Carnio, Vinod Bajaj, Domenico Marsella, Sergei K. Turitsyn, Jaroslaw E. Prilepsky, Vahid Aref. 2021-07-26. End-to-End Deep Learning of Long-Haul Coherent Optical Fiber Communications via Regular Perturbation Model. https://arxiv.org/abs/2107.12320

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