TY - GEN
T1 - Modulation Format Identification Based on Channel-spatial Attention Modules and Deep Learning
AU - Chen, Qian
AU - Zhang, Qi
AU - Xin, Xiangjun
AU - Cui, Yi
AU - Wang, Fu
AU - Tian, Feng
AU - Tian, Qinghua
AU - Wang, Yongjun
AU - Yang, Leijing
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - In this paper, a scheme is proposed where the channel and spatial attention convolutional neural networks are applied to identify modulation formats from signal constellation diagrams. According to the simulation results, it outperforms other modulation format identification (MFI) schemes in the overall identification rate. Moreover, the scheme shows a significant advantage in signal identification given low optical signal-to-noise ratios (OSNRs).
AB - In this paper, a scheme is proposed where the channel and spatial attention convolutional neural networks are applied to identify modulation formats from signal constellation diagrams. According to the simulation results, it outperforms other modulation format identification (MFI) schemes in the overall identification rate. Moreover, the scheme shows a significant advantage in signal identification given low optical signal-to-noise ratios (OSNRs).
KW - convolutional neural networks
KW - modulation format identification
KW - signal constellation diagrams
UR - https://www.scopus.com/pages/publications/85182257486
U2 - 10.1109/ICCASIT58768.2023.10351587
DO - 10.1109/ICCASIT58768.2023.10351587
M3 - Conference contribution
AN - SCOPUS:85182257486
T3 - Proceedings of 2023 IEEE 5th International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2023
SP - 887
EP - 891
BT - Proceedings of 2023 IEEE 5th International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2023
A2 - Sun, Huabo
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th IEEE International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2023
Y2 - 11 October 2023 through 13 October 2023
ER -