End-to-End Deep Learning of Long-Haul Coherent Optical Fiber Communications via Regular Perturbation Model-Reference-Cited by-同舟云学术

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

Published:2021-09-13 Issue: Volume: Page:
ISSN:
Container-title:2021 European Conference on Optical Communication (ECOC)
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Author:

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

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9605697/9605699/09605928.pdf?arnumber=9605928

Cited by 14 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Bi-GRU Enhanced Cost-Effective Memory-Aware End-to-End Learning for Geometric Constellation Shaping in Optical Coherent Communications;IEEE Photonics Journal;2024-02

2. End-to-End Learning of Constellation Shaping for Optical Fiber Communication Systems;IEEE Photonics Journal;2023-12

3. Wasserstein Autoencoder Based End-to-End Learning Strategy of Geometric Shaping for an OAM Mode Division Multiplexing IM/DD Transmission;2023 Asia Communications and Photonics Conference/2023 International Photonics and Optoelectronics Meetings (ACP/POEM);2023-11-04

4. A CGAN-aided Autoencoder Supporting Joint Geometric Probabilistic Shaping for Optical Fiber Communication System;2023 Asia Communications and Photonics Conference/2023 International Photonics and Optoelectronics Meetings (ACP/POEM);2023-11-04

5. Machine learning meets photonics;Emerging Topics in Artificial Intelligence (ETAI) 2023;2023-09-28

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