A Survey on Deep Learning Techniques in Wireless Signal Recognition
Joint Authors
Li, Xiaofan
Dong, Fangwei
Zhang, Sha
Guo, Weibin
Source
Wireless Communications and Mobile Computing
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-02-17
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Information Technology and Computer Science
Abstract EN
Wireless signal recognition plays an important role in cognitive radio, which promises a broad prospect in spectrum monitoring and management with the coming applications for the 5G and Internet of Things networks.
Therefore, a great deal of research and exploration on signal recognition has been done and a series of effective schemes has been developed.
In this paper, a brief overview of signal recognition approaches is presented.
More specifically, classical methods, emerging machine learning, and deep leaning schemes are extended from modulation recognition to wireless technology recognition with the continuous evolution of wireless communication system.
In addition, the opening problems and new challenges in practice are discussed.
Finally, a conclusion of existing methods and future trends on signal recognition is given.
American Psychological Association (APA)
Li, Xiaofan& Dong, Fangwei& Zhang, Sha& Guo, Weibin. 2019. A Survey on Deep Learning Techniques in Wireless Signal Recognition. Wireless Communications and Mobile Computing،Vol. 2019, no. 2019, pp.1-12.
https://search.emarefa.net/detail/BIM-1212184
Modern Language Association (MLA)
Li, Xiaofan…[et al.]. A Survey on Deep Learning Techniques in Wireless Signal Recognition. Wireless Communications and Mobile Computing No. 2019 (2019), pp.1-12.
https://search.emarefa.net/detail/BIM-1212184
American Medical Association (AMA)
Li, Xiaofan& Dong, Fangwei& Zhang, Sha& Guo, Weibin. A Survey on Deep Learning Techniques in Wireless Signal Recognition. Wireless Communications and Mobile Computing. 2019. Vol. 2019, no. 2019, pp.1-12.
https://search.emarefa.net/detail/BIM-1212184
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1212184