Corpus ID: 236428958

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

@article{Neskorniuk2021EndtoEndDL,
  title={End-to-End Deep Learning of Long-Haul Coherent Optical Fiber Communications via Regular Perturbation Model},
  author={Vladislav Neskorniuk and A M C{\'a}rnio and Vinod Bajaj and Domenico Marsella and Sergei K. Turitsyn and Jaroslaw E. Prilepsky and Vahid Aref},
  journal={ArXiv},
  year={2021},
  volume={abs/2107.12320}
}
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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