Open Access
Volume 40, Number 6, December 2022
Page(s) 1375 - 1384
Published online 10 February 2023
  1. MILCZAREK H, LESNIK C, DJUROVIC L, et al. Estimating the instantaneous frequency of linear and nonlinear frequency modulated radar signals-a comparative study[J]. Sensors, 2021, 21(8): 2840 [CrossRef] [Google Scholar]
  2. JIANG Xinrui, CHEN Hui, ZHAO Yaodong, et al. Automatic modulation recognition based on mixed-type features[J]. International Journal of Electronics, 2021, 108(1): 105–114. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  3. O'SHEA T J, CORGAN J, and CLANCY T C. Convolutional radio modulation recognition networks[C]// 17th International Conference on Engineering Applications of Neural Networks, Aberdeen, 2016 [Google Scholar]
  4. O'SHEA T J, ROY T, CLANCY T C. Over-the-air deep learning based radio signal classification[J]. IEEE Journal of Selected Topics in Signal Process, 2018, 12(1): 168–179. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  5. BAI Pengyuan, XU Hua, SUN Li. A recognition algorithm for modulation schemes by convolution neural network and spectrum texture[J]. Journal of Northwestern Polytechnical University, 2019, 37(4): 816–823. [Article] [Article] (in Chinese) [Google Scholar]
  6. KRZYSTON J, BHATTACHARJEA R, STARK A. Complex-valued convolutions for modulation recognition using deep learning[C]//2020 IEEE International Conference on Communications Workshops, Dublin, Ireland, 2020 [Google Scholar]
  7. LIANG Zhi, TAO Mingliang, WANG Ling, et al. Automatic modulation recognition based on adaptive attention mechanism and ResNext WSL model[J]. IEEE Communications Letters, 2021, 25(9): 2953–2957. [Article] [CrossRef] [Google Scholar]
  8. LIU Kai, GAO Wanjun, HUANG Qinghua. Automatic modulation recognition based on a DCN-BiLSTM network[J]. Sensors, 2021, 21(5): 1577 [NASA ADS] [CrossRef] [Google Scholar]
  9. NJOKU J N, MOROCHO-CAYAMCELA M E, LIM W. CGDNet: efficient hybrid deep learning model for robust automatic modulation recognition[J]. IEEE Networking Letters, 2021, 3(2): 47–51. [Article] [CrossRef] [Google Scholar]
  10. LI Dongjin, YANG Ruijuan, LI Xiaobai, et al. Radar signal modulation recognition based on deep joint learning[J]. IEEE Access, 2020(8): 48515–48528 [Google Scholar]
  11. ROYLE J A, DORAZIO R M, LINK W A. Analysis of multinomial models with unknown index using data augmentation[J]. Journal of Computational and Graphical Statistics, 2007, 16(1): 67–85. [Article] [Google Scholar]
  12. YOSINSKI J, CLUNE J, BENGIO Y, et al. How transferable are features in deep neural networks?[C]//28th Conference on Neural Information Processing Systems, Montreal, Canada, 2014 [Google Scholar]
  13. SHI Yunhao, XU Hua, LIU Yinghui. A few-shot modulation recognition method based on pseudo-label semi-supervised learning[J]. Journal of Northwestern Polytechnical University, 2020, 38(5): 1074–1083. [Article] [Article] (in Chinese) [Google Scholar]
  14. MENG Fan, CHEN Peng, WU Lenan, et al. Automatic modulation classification: a deep learning enabled approach[J]. IEEE Trans on Vehicular Technology, 2018, 67(11): 10760–10772. [Article] [CrossRef] [Google Scholar]
  15. YU Xu, LI Dezhi, WANG Zhenyong, et al. A deep learning method based on convolutional neural network for automatic modulation classification of wireless signals[J]. Wireless Networks, 2019, 25(7): 3735–3746. [Article] [CrossRef] [Google Scholar]
  16. HUISMAN M, VAN RIJN J N, PLAAT A. A survey of deep meta-learning[J]. Artificial Intelligence Review, 2021, 54(6): 1–59 [Google Scholar]
  17. CHEN Yutian, HOFFMAN M W, COLMENAREJO S G, et al. Learning to learn by gradient descent by gradient descent[C]//34th International Conference on Machine Learning, Sydney, Australia, 2017 [Google Scholar]
  18. FINN C, ABBEEL P, LEVINE S. Model-agnostic meta-learning for fast adaptation of deep networks[C]//International Conference on Machine Learning, PMLR, 2017 [Google Scholar]
  19. SNELL J, SWERSKY K, ZEMEL R. Prototypical networks for few-shot learning[C]//31st Annual Conference on Neural Information Processing Systems, Long Beach, CA, 2017 [Google Scholar]
  20. HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, US, 2016 [Google Scholar]
  21. 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, 2021 [Google Scholar]
  22. VINYALS O, BLUNDELL C, LILLICRAP T, et al. Matching networks for one shot learning[C]//30th Conference on Neural Information Processing Systems, Barcelona Spain, 2016 [Google Scholar]
  23. YANG Ning, ZHANG Bangning, DING Guoru, et al. Specific emitter identification with limited samples: a model-agnostic meta-learning approach[J]. IEEE Communications Letters, 2022, 26(2): 345–349. [Article] [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.