End-to-End Learning in Optical Fiber Communications: Experimental Demonstration and Future Trends

被引：8
|
作者：
Karanov, Boris [1 ,2 ,3 ]
Oliari, Vinicius [2 ]
Chagnon, Mathieu [3 ]
Liga, Gabriele [2 ]
Alvarado, Alex [2 ]
Aref, Vahid [3 ]
Lavery, Domanic [1 ]
Bayvel, Polina [1 ]
Schmalen, Laurent [4 ]
机构：
[1] UCL, Dept Elect & Elect Engn, Opt Networks Grp, London WC1E 7JE, England
[2] Eindhoven Univ Technol, NL-5600 MB Eindhoven, Netherlands
[3] Nokia Bell Labs, D-70435 Stuttgart, Germany
[4] Karlsruhe Inst Technol, Commun Engn Lab, D-76131 Karlsruhe, Germany
来源：
2020 EUROPEAN CONFERENCE ON OPTICAL COMMUNICATIONS (ECOC) | 2020年
基金：
欧洲研究理事会; 欧盟地平线“2020”; 英国工程与自然科学研究理事会;
关键词：
D O I：
10.1109/ECOC48923.2020.9333265
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Fiber-optic auto-encoders are demonstrated on an intensity modulation/direct detection test-bed, outperforming state-of-the-art signal processing. Algorithms for end-to-end optimization using experimentally collected data are discussed. The end-to-end learning framework is extended for performing optimization of the symbol distribution in probabilistically-shaped coherent systems.
引用
收藏
页数：4
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