基于卷积神经网络架构
CNN
参考文献
T.J. O’Shea, J. Corgan, T.C. Clancy, Convolutional radio modulation recognition networks, in: Proc. Int. Conf. Eng. Appl. Neural Netw., Springer, 2016, pp. 213–226.
MCNet
参考文献
T. Huynh-The, C.-H. Hua, Q.-V. Pham, and D.-S. Kim, “MCNet: An efficient CNN architecture for robust automatic modulation classification,” IEEE Commun. Lett., vol. 24, no. 4, pp. 811–815, Apr. 2020.
IC-AMCNet
参考文献
A.P. Hermawan, R.R. Ginanjar, D.S. Kim, J.M. Lee, CNN-based automatic modulation classification for beyond 5G communications, IEEE Commun. Lett. 24 (2020) 1038–1041.
ResNet
参考文献
X. Liu, D. Yang, A. El Gamal, Deep neural network architectures for modulation classification, in: Proc. 51st Asilomar Conf. Signals, Syst., Comput., 2017, pp. 915–919.
DenseNet
参考文献
X. Liu, D. Yang, A. El Gamal, Deep neural network architectures for modulation classification, in: Proc. 51st Asilomar Conf. Signals, Syst., Comput., 2017, pp. 915–919.
基于循环神经网络架构
GRU
参考文献
D. Hong, Z. Zhang, and X. Xu, “Automatic modulation classification using recurrent neural networks,” in Proc. 3rd IEEE Int. Conf. Comput. Commun. (ICCC), 2017, pp. 695–700.
LSTM
参考文献
S. Rajendran, W. Meert, D. Giustiniano, V. Lenders, S. Pollin, Deep learning models for wireless signal classification with distributed low-cost spectrum sensors, IEEE Trans. Cogn. Commun. Netw. 4 (2018) 433–445.
DAE
参考文献
Z. Ke, H. Vikalo, Real-time radio technology and modulation classification via an LSTM auto-encoder, IEEE Trans. Wirel. Commun. 21 (2022) 370–382.
基于混合神经网络架构
CLDNN
一种混合网络架构，直接使用IQ信号进行识别。
- CNN 提取局部时频特征；
- LSTM 捕捉时间依赖；
- DNN 输出最终预测。
参考文献
X. Liu, D. Yang, and A. El Gamal, “Deep neural network architectures for modulation classification,” in Proc. 51st Asilomar Conf. Signals, Syst., Comput., 2017, pp. 915–919.
架构图
