In this paper, we designed a ultra lite CNN (ULCNN) (9,751 trainable parameters and 0.2M MACCs) for AMC, and its simulation is based on RML2016.10a
http://arxiv.org/abs/2208.04659
L. Guo, Y. Wang, Y. Liu, Y. Lin, H. Zhao and G. Gui, "Ultralight Convolutional Neural Network for Automatic Modulation Classification in Internet of Unmanned Aerial Vehicles," in IEEE Internet of Things Journal, vol. 11, no. 11, pp. 20831-20839, June 2024.
keras=2.1.4 tensorflow=1.14
MCLDNN [1]
SCNN [2]
MCNet [3]
PET-CGDNN [4]
ULCNN is the proposed structure.
The model weights are given in "model/"
RML2016.10a
Train/val/test samples: 77000/33000/110000
https://pan.baidu.com/s/1T36jgWlZ3oWmFWYpQLyiZg, passwd：f7qy or run dataset2016.py
[1] J. Xu, C. Luo, G. Parr and Y. Luo, "A Spatiotemporal Multi-Channel Learning Framework for Automatic Modulation Recognition," in IEEE Wireless Communications Letters, vol. 9, no. 10, pp. 1629-1632, Oct. 2020, doi: 10.1109/LWC.2020.2999453.
[2] X. Fu et al., "Lightweight Automatic Modulation Classification Based on Decentralized Learning," in IEEE Transactions on Cognitive Communications and Networking, vol. 8, no. 1, pp. 57-70, March 2022, doi: 10.1109/TCCN.2021.3089178.
[3] T. Huynh-The, C. Hua, Q. Pham and D. Kim, "MCNet: An Efficient CNN Architecture for Robust Automatic Modulation Classification," in IEEE Communications Letters, vol. 24, no. 4, pp. 811-815, April 2020, doi: 10.1109/LCOMM.2020.2968030.
[4] F. Zhang, C. Luo, J. Xu and Y. Luo, "An Efficient Deep Learning Model for Automatic Modulation Recognition Based on Parameter Estimation and Transformation," in IEEE Communications Letters, vol. 25, no. 10, pp. 3287-3290, Oct. 2021, doi: 10.1109/LCOMM.2021.3102656.
Note that our code is partly based on leena201818, wzjialang, ThienHuynhThe and Richardzhangxx.
Thanks for your great works!