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Monitoring I/Q data and pulse carving misalignments in RZ-DQPSK transmitters using a neural network approach
Published
Author(s)
Jeffrey A. Jargon, Xiaoxia Wu, Louis Christen, Alan Willner, Loukas Paraschis
Abstract
We propose a technique using artificial neural networks (ANNs) to simultaneously identify I/Q data misalignment and data/carver misalignment in both parallel-type and serial-type RZ-DQPSK transmitters. A correlation coefficient of 0.99 is obtained by using a 3-input ANN for the parallel case and a 2-input ANN for the serial case.
Proceedings Title
The 21st Annual Meeting of The IEEE Lasers & Electro-Optics Society
Jargon, J.
, Wu, X.
, Christen, L.
, Willner, A.
and Paraschis, L.
(2008),
Monitoring I/Q data and pulse carving misalignments in RZ-DQPSK transmitters using a neural network approach, The 21st Annual Meeting of The IEEE Lasers & Electro-Optics Society, Newport Beach, CA, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=33152
(Accessed October 20, 2025)