Journal
JOURNAL OF LIGHTWAVE TECHNOLOGY
Volume 35, Issue 4, Pages 868-875Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JLT.2016.2590989
Keywords
Machine learning; neural networks; optical communication; performance monitoring; support vector machines
Funding
- Villum Foundation Young Investigator program
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Linear signal processing algorithms areeffective in dealing with linear transmission channel and linear signal detection, whereas the nonlinear signal processing algorithms, from the machine learning community, are effective in dealing with nonlinear transmission channel and nonlinear signal detection. In this paper, a brief overview of the various machine learning methods and their application in optical communication is presented and discussed. Moreover, supervised machine learning methods, such as neural networks and support vector machine, are experimentally demonstrated for in-band optical signal to noise ratio estimation and modulation format classification, respectively. The proposed methods accurately evaluate optical signals employing up to 64 quadrature amplitude modulation, at 32 Gbd, using only directly detected data.
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