[1] [1] ZHOU Huaji, JIAO Licheng, ZHENG Shilian, et al. Weight-variable scattering convolution networks and its application in electromagnetic signal classification[J]. IEEE Access, 2019(7):175889-175896.

[2] [2] ZHENG Shilian, CHEN Shichuan, YANG Lifeng, et al. Big data processing architecture for radio signals empowered by deep learning: concept,experiment,applications and challenges[J]. IEEE Access, 2018(6):55907-55922.

[3] [3] ZHENG Shilian,CHEN Shichuan,QI Peihan,et al. Spectrum sensing based on deep learning classification for cognitive radios[J].China Communications, 2020,17(2):138-148.

[4] [4] RAJENDRAN S,MERRT W,GIUSTINIANO D,et al. Deep learning models for wireless signal classification with distributed low- cost spectrum sensors[J]. IEEE Transactions on Cognitive Communications and Networking, 2018,4(3):433-445.

[5] [5] NISHIZAKI H,MAKINO K. Signal classification using deep learning[C]// 2019 IEEE International Conference on Sensors and Nanotechnology. Penang,Malaysia:IEEE, 2019:1-4.

[7] [7] CHEN Shichuan,ZHENG Shilian,YANG Lifeng,et al. Deep learning for large-scale real-world ACARS and ADS-B radio signal classification[J]. IEEE Access, 2019(7):89256-89264.

[8] [8] ZHOU Huaji,JIAO Licheng,ZHENG Shilian,et al. Generative adversarial network-based electromagnetic signal classification:a semi-supervised learning framework[J]. China Communications, 2020,17(10):157-169.

[9] [9] RAHMAN S, KHAN S,PORIKLI F. A unified approach for conventional zero-shot, generalized zero-shot, and few-shot learning[J].IEEE Transactions on Image Processing, 2018,27(11):5652-5667.

[10] [10] ZHANG Zilin,LI Yan,GAO Meiguo. Few-shot learning of signal modulation recognition based on attention relation network[C]// 2020 28th European Signal Processing Conference(EUSIPCO). Amsterdam,Netherlands:[s.n.], 2021:1372-1376.

[11] [11] SHI Yunhao, XU Hua, JIANG Lei, et al. Few-shot modulation classification method based on feature dimension reduction and pseudo-label training[J]. IEEE Access, 2020,8:140411-140425.

[12] [12] GOODFELLOW I J,POUGET-ABADIE J,MIRZA M,et al. Generative adversarial nets[C]// International Conference on Neural Information Processing Systems(NIPS). Cambridge,MA,USA:MIT Press, 2014:2672-2680.

[13] [13] WANG Chaoyue,XU Chang,YAO Xin,et al. Evolutionary generative adversarial networks[J]. IEEE Transactions on Evolutionary Computation, 2019,23(6):921-934.

[16] [16] FERNANDO T,SRIDHARAN S,MCLAREN M,et al. Temporarily-aware context modeling using generative adversarial networks for speech activity detection[J]. IEEE/ACM Transactions on Audio,Speech,and Language Processing, 2020,28(1):1159-1169.

[17] [17] TANG Bin, TU Ya, ZHANG Zhaoyue, et al. Digital signal modulation classification with data augmentation using generative adversarial nets in cognitive radio networks[J]. IEEE Access, 2018,6(1):15713-15722.

[18] [18] YAO Xiaohui, YANG Honghui, LI Yiqing. Modulation identification of underwater acoustic communications signals based on generative adversarial networks[C]// 2019 OCEANS. Marseille,France:IEEE, 2019:1-6.

[19] [19] ODENA A,OLAH C,SHLENS J. Conditional image synthesis with auxiliary classifier GANs[C]// 2017 International Conference on Machine Learning(ICML). Sydney,Australia:Scoups, 2017:4043-4055.

[20] [20] PEZZOTTI N,LELIEVELDT B P F,MAATEN L V D,et al. Approximated and user steerable tSNE for progressive visual analytics[J]. IEEE Transactions on Visualization and Computer Graphics, 2017,23(7):1739-1752.

[21] [21] OSHEA T, HOYDIS J. An introduction to deep learning for the physical layer[J]. IEEE Transactions on Cognitive Communications and Networking, 2017,3(4):563-575.