{"title":"Wireless Signal Service Type Identification Based on Convolutional Neural Network","authors":"Haomin Tian, Liang Yin, Xiaofeng Yang, Shufang Li, Haoyang Yu","doi":"10.1109/ICEICT.2019.8846371","DOIUrl":null,"url":null,"abstract":"Nowadays, with the birth of 5G and the Internet of Things, more and more business signals have emerged. The identification of signal service types has become a hot research topic. Whether it is suitable for daily life in the military field, there is a wide application prospect. Using the power spectrum data of the wireless signal, the characteristics of the power spectrum waveform are captured to identify the type of traffic of the wireless signal. This paper proposes the use of convolutional neural networks to extract and classify the wireless signal power spectrum data. After hundreds of iterative training, it can achieve an ideal recognition effect.","PeriodicalId":382686,"journal":{"name":"2019 IEEE 2nd International Conference on Electronic Information and Communication Technology (ICEICT)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE 2nd International Conference on Electronic Information and Communication Technology (ICEICT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICEICT.2019.8846371","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Nowadays, with the birth of 5G and the Internet of Things, more and more business signals have emerged. The identification of signal service types has become a hot research topic. Whether it is suitable for daily life in the military field, there is a wide application prospect. Using the power spectrum data of the wireless signal, the characteristics of the power spectrum waveform are captured to identify the type of traffic of the wireless signal. This paper proposes the use of convolutional neural networks to extract and classify the wireless signal power spectrum data. After hundreds of iterative training, it can achieve an ideal recognition effect.