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Proceedings Paper

Applications of machine-learning in optical communications and networks
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Paper Abstract

We discuss various applications of machine learning techniques in different aspects of optical communications and networking including optical performance monitoring, fiber nonlinearity compensation, cognitive network failure prediction, dynamic planning and cross-layer optimization of software-defined networks, quality of transmission estimation, and physical layer design of optical communication systems. Recent works employing deep learning technologies are also discussed.

Paper Details

Date Published: 31 January 2020
PDF: 8 pages
Proc. SPIE 11309, Next-Generation Optical Communication: Components, Sub-Systems, and Systems IX, 113090L (31 January 2020); doi: 10.1117/12.2544102
Show Author Affiliations
Faisal Nadeem Khan, The Hong Kong Polytechnic Univ. (Hong Kong, China)
Qirui Fan, The Hong Kong Polytechnic Univ. (Hong Kong, China)
Alan Pak Tao Lau, The Hong Kong Polytechnic Univ. (Hong Kong, China)
Chao Lu, The Hong Kong Polytechnic Univ. (Hong Kong, China)


Published in SPIE Proceedings Vol. 11309:
Next-Generation Optical Communication: Components, Sub-Systems, and Systems IX
Guifang Li; Xiang Zhou, Editor(s)

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