[1] [1] SUN Y L, LIU J. Automatic recognition of signal modulation based on deep learning[J]. Journal of Qilu University of Technology, 2020, 34(01): 58-63. (in Chinese)

[2] [2] ZENG C Z, JIA X, ZHU W G. Modulation classification of communication signals[J]. Communications technology, 2015, 48(03): 252-257. (in Chinese)

[3] [3] LU Y X, ZHANG H Y, LIANG Y, et al. Indoor visible light channel model of convolutional neural network based on GAN fingerprint database[J]. Journal of optoelectronics·laser, 2023, 34(11): 1201-1209. (in Chinese)

[4] [4] CHEN D, WANG C H, YAO B Y. Wireless optical subcarrier modulation identification based on fuzzy clustering and BP neural network[J]. Laser & optoelectronic progress, 2018, 55(06): 61-66. (in Chinese)

[5] [5] YANG H J, XU J L, LIU Y L. Modulation recognition of optical communication system based on pattern recognition[J]. Laser journal, 2019, 40(03): 109-112. (in Chinese)

[6] [6] LI T Y, LI M, ZHOU M O, et al. Signal detection in ACO-OFDM free-space optical communication based on deep learning[J]. Journal of optoelectronics·laser, 2023, 34(01): 68-73. (in Chinese)

[7] [7] LI Y, DONG Q, DUAN L Y. Research on state pattern recognition of an optical communication system based on cloud computing[J]. Laser journal, 2021, 42(05): 88-91. (in Chinese)

[8] [8] WANG Y, LV T T, YANG H. Classification of underwater communication modulated signals based on deep residual network[J]. Digital ocean & underwater warfare, 2020, 3(03): 242-249. (in Chinese)

[9] [9] ZHU L, HAN H M, PENG H. Research on QPSK intelligent receiver model based on deep learning[J]. Computer measurement and control, 2024, 32(02): 213-218. (in Chinese)

[10] [10] GAO L, NI H. Research on fault diagnosis of optical communication system based on improved convolutional neural network[J]. Laser magazine, 2023, 44(08): 161-165. (in Chinese)

[11] [11] YUN L, TU Y, ZHENG D, et al. The application of deep learning in communication signal modulation recognition[C]//2017 IEEE/CIC International Conference on Communications in China (ICCC), October 22-24, 2017, Qingdao, China. New York: IEEE, 2017: 8330488.

[12] [12] ZHANG W T, CUI D. Training images generation for CNN based automatic modulation classification[J]. IEEE access, 2021, 9: 62916-62925.

[13] [13] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[C]//International Conference on Neural Information Processing Systems, December 3-6, 2012, Lake Tahoe, Nevada, USA. Vancouver: NIPS, 2012: 1097-1105.

[14] [14] SZEGEDY C, LIU W, JIA Y, et al. Going deeper with convolutions[C]//Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition, June 8-10, 2015, Boston, MA, USA. New York: IEEE, 2015: 1-9.

[15] [15] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[EB/OL]. (2015-04-10) [2024-01-29]. https://arxiv.org/pdf/1409.1556.pdf.

[16] [16] HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, June 26-July 1, 2016, Las Vegas, NV, USA. New York: IEEE, 2016: 770-778.