Add to BibSonomyAbstract
A technique using artificial neural networks trained with parameters derived from reconstructed eye diagrams for multi-impairment monitoring in a 40-Gbps RZ-OOK system is demonstrated. The proposed monitoring scheme can accurately identify simultaneous impairments without requiring data clock recovery.
© 2011 AOS
PDF ArticleMore Like This
Optical performance monitoring in 40-Gbps optical duobinary system using artificial neural networks trained with reconstructed eye diagram parameters
Jun-sen Lai, Ai-ying Yang, Lin Zuo, and Yu-nan Sun
83100M Asia Communications and Photonics Conference and Exhibition (ACP) 2011
Experimental Comparison of Performance Monitoring Using Neural Networks Trained with Parameters Derived from Delay-Tap Plots and Eye Diagrams
Xiaoxia Wu, Jeffrey A. Jargon, Chih-Ming Wang, Loukas Paraschis, and Alan E. Willner
JThA17 National Fiber Optic Engineers Conference (NFOEC) 2010
Optical Performance Monitoring of 56Gbps Optical PAM4 Signal Using Artificial Neural Networks
Yongtao Huang, YuanXiang Chen, and Jianguo Yu
M1H.1 Asia Communications and Photonics Conference (ACP) 2017