The application of deep learning in communication signal modulation recognition
Document Type
Conference Proceeding
Publication Date
4-2-2018
Abstract
Automated Modulation Classification (AMC) has been applied in various emerging areas such as cognitive radio (CR). We also notice that Deep Learning (DL) is a powerful classification tool that has gained great popularity in various field. This article focuses on DL and aims at using it to solve communications problems. We propose a new data conversion algorithm in order to gain a better classification accuracy of communication signal modulation. This paper will show that our new method will bring significant improvement in signal modulation classification accuracy. Besides, AlexNet and GoogLeNet, two well-known DL network models, ResNet and VGG, will be utilized in this task to compare with each other.
Repository Citation
Lin, Y.,
Tu, Y.,
Dou, Z.,
& Wu, Z.
(2018). The application of deep learning in communication signal modulation recognition. 2017 IEEE/CIC International Conference on Communications in China, ICCC 2017, 2018-January, 1-5.
https://corescholar.libraries.wright.edu/ee/61
DOI
10.1109/ICCChina.2017.8330488
