Geometric Constellation Shaping for Fiber-Optic Channels via End-to-End Learning
Author:
Affiliation:
1. Department of Electrical and Photonic Engineering, Technical University of Denmark, Lyngby, Denmark
Funder
European Research Council
ERC-CoG FRECOM
Villum Young Investigator OPTIC-AI project
DNRF SPOC
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Atomic and Molecular Physics, and Optics
Link
http://xplorestaging.ieee.org/ielx7/50/10164793/10124361.pdf?arnumber=10124361
Reference53 articles.
1. End-to-end learning of geometrical shaping maximizing generalized mutual information;gümüs;Proc IEEE Opt Fiber Commun Conf Exhib,0
2. Neural Network Assisted Geometric Shaping for 800Gbit/s and 1Tbit/s Optical Transmission
3. Autoencoder-Optimized Geometric Constellation Shaping for Unamplified Coherent Optical Links
4. End-to-End Deep Learning of Long-Haul Coherent Optical Fiber Communications via Regular Perturbation Model
5. Rate Adaptation and Reach Increase by Probabilistically Shaped 64-QAM: An Experimental Demonstration
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