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Compressive subspace learning based wideband spectrum sensing for multiantenna cognitive radio

文献类型：期刊论文

作者Zheng M(郑萌)2,3; Yang ZJ(杨志家)2,3; Gong TR(宫铁瑞)1,2,3
刊名IEEE Transactions on Vehicular Technology
出版日期2019
卷号68期号:7页码:6636-6648
关键词Wideband spectrum sensing compressive subspace learning sub-Nyquist sampling multiantenna, cognitive radio
ISSN号0018-9545
产权排序1
英文摘要Recently, sub-Nyquist sampling (SNS) based wideband spectrum sensing has emerged as a promising approach for cognitive radios. However, most of existing SNS-based approaches cannot effectively deal with the wireless channel fading due to the lack of space diversity exploitation, which would lead to poor sensing performance. To address the problem, we propose a multiantenna system, referred to as the multiantenna generalized modulated converter (MAGMC), to realize the SNS, where spatially correlated multiple-input multiple-output (MIMO) channel is considered. Based on the multiantenna system, two compressive subspace learning (CSL) approaches (mCSL and vCSL) are proposed for signal subspace learning, where wideband sectrum sensing is realized based on the signal subspace. Both proposed CSL approaches exploit space diversity, where the mCSL utilizes an antenna averaging temporal decomposition, and the vCSL (which is formulated based on a vectorization of sample matrix in the mCSL) uses a spatial-temporal joint decomposition. We further establish analytical relationships between eigenvalues of statistical covariance matrices in statistical sense in both multiantenna and single antenna scenarios. Space diversity and superiority over the single antenna scenario for both proposed CSL approaches are analyzed based on the derived analytical relationships. Moreover, the mCSL and vCSL based wideband spectrum sensing algorithms are proposed based on the system model of MAGMC and their computational complexities are given. The proposed CSL based wideband spectrum sensing algorithms can effectively deal with the wireless channel fading and simulations show the improvement on performance of wideband spectrum sensing over related works.
WOS关键词NETWORKS ; DESIGN
资助项目National Key Research and Development Program of China[2017YFA0700304] ; National Natural Science Foundation of China[61673371] ; International Partnership Program of Chinese Academy of Sciences[173321KYSB20180020] ; Youth Innovation Promotion Association, Chinese Academy of Sciences[2015157] ; Liaoning Provincial Natural Science Foundation of China[20170540662]
WOS研究方向Engineering ; Telecommunications ; Transportation
语种英语
WOS记录号WOS:000476775000034
资助机构National Key Research and Development Program of China ; National Natural Science Foundation of China ; International Partnership Program of Chinese Academy of Sciences ; Youth Innovation Promotion Association, Chinese Academy of Sciences ; Liaoning Provincial Natural Science Foundation of China
源URL[http://ir.sia.cn/handle/173321/25311]  
专题沈阳自动化研究所_工业控制网络与系统研究室
通讯作者Zheng M(郑萌); Yang ZJ(杨志家)
作者单位1.University of Chinese Academy of Sciences, Beijing 100049, China
2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China
3.State Key Lab of Robotics, Key Lab of Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
推荐引用方式
GB/T 7714		 Zheng M,Yang ZJ,Gong TR. Compressive subspace learning based wideband spectrum sensing for multiantenna cognitive radio[J]. IEEE Transactions on Vehicular Technology,2019,68(7):6636-6648.
APA Zheng M,Yang ZJ,&Gong TR.(2019).Compressive subspace learning based wideband spectrum sensing for multiantenna cognitive radio.IEEE Transactions on Vehicular Technology,68(7),6636-6648.
MLA Zheng M,et al."Compressive subspace learning based wideband spectrum sensing for multiantenna cognitive radio".IEEE Transactions on Vehicular Technology 68.7(2019):6636-6648.

入库方式： OAI收割

来源：沈阳自动化研究所

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