An Interpretable Mapping From a Communication System to a Neural Network for Optimal Transceiver-Joint Equalization-Reference-Cited by-同舟云学术

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An Interpretable Mapping From a Communication System to a Neural Network for Optimal Transceiver-Joint Equalization

Published:2021-09-01 Issue:17 Volume:39 Page:5449-5458
ISSN:0733-8724
Container-title:Journal of Lightwave Technology
language:
Short-container-title:J. Lightwave Technol.

Author:

Zhai Zhiqun,Jiang Hexun,Fu Mengfan,Liu Lei,Yi Lilin^ORCID,Hu Weisheng^ORCID,Zhuge Qunbi^ORCID

Funder

National Key R&D Program of China

National Natural Science Foundation of China

Shanghai Rising-Star Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Atomic and Molecular Physics, and Optics

Link

http://xplorestaging.ieee.org/ielx7/50/9536595/09447958.pdf?arnumber=9447958

Cited by 10 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. End-to-End Joint Digital and Optical Signal Processing Enabled by Interpretable Deep Learning for Coherent Optical Communication Systems;Optical Fiber Communication Conference (OFC) 2024;2024

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

3. Transceiver-Impairment Mitigation Enabled by Adaptive Symbol Decision with Neural Networks;2023 International Conference on Photonics in Switching and Computing (PSC);2023-09-26

4. Joint-Transceiver Equalization Technique over a 1.4 km Multi-Mode Fiber Using Optical MIMO Technique in IM/DD Systems;Photonics;2023-06-19

5. Low-cost Coherent Transceivers with Quantization Noise Mitigation Function Enabled by Neural Networks;Advanced Photonics Congress 2023;2023

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