Review of deep learning in cognitive radio

LIU Bo; BAI Xiaodong; ZHANG Gengxin; SHEN Jun; XIE Jidong; ZHAO Laiding; HONG Tao

doi:10.3969/j.issn.1000-5641.201922017

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Article Navigation > Journal of East China Normal University (Natural Sciences) > 2021 > (1): 36-52

LIU Bo, BAI Xiaodong, ZHANG Gengxin, SHEN Jun, XIE Jidong, ZHAO Laiding, HONG Tao. Review of deep learning in cognitive radio[J]. Journal of East China Normal University (Natural Sciences), 2021, (1): 36-52. doi: 10.3969/j.issn.1000-5641.201922017

Citation:

LIU Bo, BAI Xiaodong, ZHANG Gengxin, SHEN Jun, XIE Jidong, ZHAO Laiding, HONG Tao. Review of deep learning in cognitive radio[J]. Journal of East China Normal University (Natural Sciences), 2021, (1): 36-52. doi: 10.3969/j.issn.1000-5641.201922017

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Review of deep learning in cognitive radio

doi: 10.3969/j.issn.1000-5641.201922017

1.
College of Telecommunication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing　210003, China
2.
CAST-Xi’an Institute of Space Radio Technology, Xi’an　710000, China

Received Date: 2019-11-16
Available Online: 2020-06-06
Publish Date: 2021-01-27

Abstract

Abstract

The development of wireless communication has made spectrum resources increasingly scarce. Existing spectrum resources, however, are not currently used in an efficient way. This contradiction can usually be attributed to the problem created by static spectrum allocation strategies. Cognitive radio (CR) is widely regarded as a feasible solution to solve the problem of static spectrum allocation. In recent years, deep learning, an emerging field of machine learning, has contributed to a number of notable research and application achievements. It has become one of the driving technologies behind artificial intelligence. In this paper, we investigated the application of deep learning to CR; this includes the development of cognitive radio and deep learning as well as the usage of deep learning models in key technologies for CR (such as spectrum prediction, spectrum environment sensing, signal analysis, etc.). Lastly, we summarize and discuss conclusions from this review.
- cognitive radio(CR),
- deep learning,
- convolutional neural network,
- recurrent neural network,
- wireless communication

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References(97)

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