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

2021 European Conference on Optical Communication (ECOC)（2021）

引用 12|浏览5

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摘要

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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关键词

learning,fiber,communications,end-to-end,long-haul

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