深度学习在认知无线电中的应用研究综述

doi:10.3969/j.issn.1000-5641.201922017

摘要/Abstract

摘要：

无线通信业务的发展使得频谱资源变得越发紧张, 而现有的频谱利用效率却不高, 这一矛盾很大程度上可归结为频谱的静态分配策略. 认知无线电(Cognitive Radio, CR)技术被广泛认为是解决频谱静态分配问题的可行方案. 深度学习作为机器学习的新兴分支, 近几年在学术界和产业界都取得了许多成果, 成为人工智能的驱动性技术之一. 对深度学习在认知无线电中的应用进行了调研, 简要介绍了认知无线电和深度学习各自的发展, 且着重介绍了深度学习算法在频谱预测、频谱环境感知、信号分析等认知无线电关键技术环节中的应用, 并在最后对此进行了总结和探讨.

关键词: 认知无线电, 深度学习, 卷积神经网络, 循环神经网络, 无线通信

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

中图分类号:

TN92

刘波, 白晓东, 张更新, 沈俊, 谢继东, 赵来定, 洪涛. 深度学习在认知无线电中的应用研究综述[J]. 华东师范大学学报（自然科学版）, 2021, 2021(1): 36-52.

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.

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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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