Review of deep learning in cognitive radio

doi:10.3969/j.issn.1000-5641.201922017

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.

Key words: cognitive radio(CR), deep learning, convolutional neural network, recurrent neural network, wireless communication

CLC Number:

TN92

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

Figures/Tables 12

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CR应用	深度学习模型	相关研究及文献
调制识别	CNN	[11-26]
	DBN	[27-29]
	RNN	GRU (Gate Recurrent Unit)^[30], LSTM^[31-32]
	其他	GAN(Generative Adversarial Networks)^[33], AE^[34-35]
频谱预测	LSTM	[36-45]
	CNN	[43,46]
	RNN	[47-48]
频谱感知	CNN	[49-55]
	DBN	[56-57]
	其他	DNN (Deep Neural Networks, DNN)^[58], LSTM+CNN^[59]
资源分配	CNN	[60-62]

特征表示	相关研究文献	特点
星座图	[14,17,20,25]	对高阶调制区分较好
循环平稳	[17,22]	适应噪声环境性能优
时频变换	[11,13,15-16]	覆盖波形种类多
瞬时特征	[24,31]	适用码率多变及大时延场景
特征自学习	[12,18-19,21,32]	预处理简单, 适用范围广

相关研究	PU先验信息	PU统计模型
Tang等^[58]	码率, 调制
Han等^[52]	码率
Do等^[49]		HMM
Lee等^[53]	PU发射功率
Liu等^[54], Ke^[59], Xie等^[55]
Liu等^[51]	OFDM帧

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