@inproceedings{5953575887c94e29836ff4835e50a495,
title = "A deep learning method based on convolutional neural network for automatic modulation classification of wireless signals",
abstract = "Automatic modulation classification (AMC) plays an important role in many fields to identify the modulation type of wireless signals. In this paper, we introduce deep learning to signal recognition. Based on architecture analysis of the convolutional neural network (CNN), we used real signal data generated by instruments as dataset, and proposed an improved CNN architecture to achieve compatible recognition accuracy of modulation classification. According to various conditions of signal noise ratio (SNR), we test the proposed CNN architecture with the real sampled signals. Experiments results show that the high-layer network is not necessary for modulation recognition with high SNR signals. The proposed CNN architecture has higher average classification accuracy than RESNET and is more compatible for modulation classification of signals with lower SNR.",
keywords = "Convolutional neural network, Deep learning, Modulation classification, Wireless signal",
author = "Yu Xu and Dezhi Li and Zhenyong Wang and Gongliang Liu and Haibo Lv",
note = "Publisher Copyright: {\textcopyright} ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018.; 2nd International Conference on Machine Learning and Intelligent Communications, MLICOM 2017 ; Conference date: 05-08-2017 Through 06-08-2017",
year = "2018",
doi = "10.1007/978-3-319-73564-1\_37",
language = "英语",
isbn = "9783319735634",
series = "Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST",
publisher = "Springer Verlag",
pages = "373--381",
editor = "Xuemai Gu and Gongliang Liu and Bo Li",
booktitle = "Machine Learning and Intelligent Communications - Second International Conference, MLICOM 2017, Proceedings",
address = "德国",
}