Automatic Modulation Classification Using Compressive Convolutional Neural Network

IEEE Access

Lu Chai¹  Zening Li¹  Zhiyong Feng²  Yifan Zhang²  Sai Huang²  Di Zhang³  Yuanyuan Yao⁴ 

[1] Daniel Felix Ritchie School of Engineering and Computer Science, University of Denver, Colorado, USA;Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China;School of Information Engineering, Zhengzhou University, Zhengzhou, China;School of Information and Communication Engineering, Beijing Information Science and Technology University, Beijing, China;

关键词: Automatic modulation classification;    compressive loss constraint;    deep convolutional neural network;    multiple constellation images;   

DOI  :  10.1109/ACCESS.2019.2921988

来源: DOAJ

【 摘 要 】

The deep convolutional neural network has strong representative ability, which can learn latent information repeatedly from signal samples and improve the accuracy of automatic modulation classification (AMC). In this paper, a novel compressive convolutional neural network (CCNN) is proposed for AMC, where different constellation images, i.e., regular constellation images (RCs) and contrast enhanced grid constellation images (CGCs), are generated as network inputs from received signals. Moreover, a compressive loss constraint is proposed to train the CCNN, which aims at capturing high-dimensional features for modulation classification. Additionally, CCNN utilizes intra-class compactness and inter-class separability to enhance the classification and robustness performance for the different orders of modulations. The simulation results demonstrate that CCNN displays superior classification and robustness performance than existing AMC methods.

【 授权许可】

Unknown