TY - JOUR
T1 - Multiparameter Monitoring of PAM4 Signals Using Deep Learning
AU - Li, Si Ao
AU - Liu, Yuanpeng
AU - Zhang, Yiwen
AU - Zhao, Wenqian
AU - Shi, Tongying
AU - Han, Xiao
AU - Djordjevic, Ivan B.
AU - Ren, Yongxiong
AU - Bao, Changjing
AU - Pan, Zhongqi
AU - Yue, Yang
N1 - Publisher Copyright:
© Optica Publishing Group 2022, © 2022 The Author(s)
PY - 2022
Y1 - 2022
N2 - PAM4 signal performance monitoring is demonstrated using CNN-based deep learning. A 98.51% prediction accuracy is achieved for jointly monitoring multiple parameters including baud rate, probabilistic shaping, roll-off factor, optical signal-to-noise ratio, and chromatic dispersion.
AB - PAM4 signal performance monitoring is demonstrated using CNN-based deep learning. A 98.51% prediction accuracy is achieved for jointly monitoring multiple parameters including baud rate, probabilistic shaping, roll-off factor, optical signal-to-noise ratio, and chromatic dispersion.
UR - https://www.scopus.com/pages/publications/85136787945
UR - https://www.scopus.com/pages/publications/85136787945#tab=citedBy
M3 - Conference article
AN - SCOPUS:85136787945
SN - 2162-2701
JO - Optics InfoBase Conference Papers
JF - Optics InfoBase Conference Papers
M1 - SW4E.3
T2 - CLEO: Science and Innovations, S and I 2022
Y2 - 15 May 2022 through 20 May 2022
ER -