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Compressive subspace learning based wideband spectrum sensing for multiantenna cognitive radio
Gong TR(宫铁瑞)1,2,3; Yang ZJ(杨志家)1,2; Zheng M(郑萌)1,2
Department工业控制网络与系统研究室
Source PublicationIEEE Transactions on Vehicular Technology
ISSN0018-9545
2019
Volume68Issue:7Pages:6636-6648
Indexed BySCI ; EI
EI Accession number20193007233642
WOS IDWOS:000476775000034
Contribution Rank1
Funding OrganizationNational 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
KeywordWideband spectrum sensing compressive subspace learning sub-Nyquist sampling multiantenna, cognitive radio
AbstractRecently, 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.
Language英语
WOS SubjectEngineering, Electrical & Electronic ; Telecommunications ; Transportation Science & Technology
WOS KeywordNETWORKS ; DESIGN
WOS Research AreaEngineering ; Telecommunications ; Transportation
Funding ProjectLiaoning Provincial Natural Science Foundation of China[20170540662] ; Youth Innovation Promotion Association, Chinese Academy of Sciences[2015157] ; International Partnership Program of Chinese Academy of Sciences[173321KYSB20180020] ; National Natural Science Foundation of China[61673371] ; National Key Research and Development Program of China[2017YFA0700304] ; Liaoning Provincial Natural Science Foundation of China[20170540662] ; Youth Innovation Promotion Association, Chinese Academy of Sciences[2015157] ; International Partnership Program of Chinese Academy of Sciences[173321KYSB20180020] ; National Natural Science Foundation of China[61673371] ; National Key Research and Development Program of China[2017YFA0700304] ; 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]
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Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/25311
Collection工业控制网络与系统研究室
Corresponding AuthorYang ZJ(杨志家); Zheng M(郑萌)
Affiliation1.State Key Lab of Robotics, Key Lab of Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China
3.University of Chinese Academy of Sciences, Beijing 100049, China
Recommended Citation
GB/T 7714
Gong TR,Yang ZJ,Zheng M. Compressive subspace learning based wideband spectrum sensing for multiantenna cognitive radio[J]. IEEE Transactions on Vehicular Technology,2019,68(7):6636-6648.
APA Gong TR,Yang ZJ,&Zheng M.(2019).Compressive subspace learning based wideband spectrum sensing for multiantenna cognitive radio.IEEE Transactions on Vehicular Technology,68(7),6636-6648.
MLA Gong TR,et al."Compressive subspace learning based wideband spectrum sensing for multiantenna cognitive radio".IEEE Transactions on Vehicular Technology 68.7(2019):6636-6648.
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