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