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Compressive Subspace Learning With Antenna Cross-Correlations for Wideband Spectrum Sensing
Gong TR(宫铁瑞)1,2,3,4,5; Yang ZJ(杨志家)1,2,3,4; Zheng M(郑萌)1,2,3,4; Liu ZF(刘志峰)1,2,3,4; Wang GS(王庚善)1,2,3,4,5
Department工业控制网络与系统研究室
Source PublicationIEEE TRANSACTIONS ON COMMUNICATIONS
ISSN0090-6778
2020
Volume68Issue:9Pages:5406-5419
Indexed BySCI ; EI
EI Accession number20204009301594
WOS IDWOS:000571738800011
Contribution Rank1
Funding OrganizationNational Key Research and Development Program of China [2017YFA0700304] ; National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [61673371] ; International Partnership Program of Chinese Academy of Sciences [173321KYSB20180020] ; Liaoning Provincial Natural Science Foundation of China [2019-YQ-09]
KeywordMIMO communication Correlation Sensors Wideband Receiving antennas Spatial diversity Compressive subspace learning wideband spectrum sensing cognitive radio MIMO antenna cross-correlation
Abstract

Compressive subspace learning (CSL) with the exploitation of space diversity has found a potential performance improvement for wideband spectrum sensing (WBSS). However, previous works mainly focus on either exploiting antenna auto-correlations or adopting a multiple-input multiple-output (MIMO) channel without considering the spatial correlations, which will degrade their performances. In this paper, we consider a spatially correlated MIMO channel and propose two CSL algorithms (i.e., mCSLSACC and vCSLACC) which exploit antenna cross-correlations, where the mCSLSACC utilizes an antenna averaging temporal decomposition, and the vCSLACC uses a spatial-temporal joint decomposition. For both algorithms, the conditions of statistical covariance matrices (SCMs) without noise corruption are derived. Through establishing the singular value relation of SCMs in statistical sense between the proposed and traditional CSL algorithms, we show the superiority of the proposed CSL algorithms. By further depicting the receiving correlation matrix of MIMO channel with the exponential correlation model, we give important closed-form expressions for the proposed CSL algorithms in terms of the amplification of singular values over traditional CSL algorithms. Such expressions provide a possibility to determine optimal algorithm parameters for high system performances in an analytical way. Simulations validate the correctness of this work and its performance improvement over existing works in terms of WBSS performance.

Language英语
WOS SubjectEngineering, Electrical & Electronic ; Telecommunications
WOS KeywordCOGNITIVE RADIO NETWORKS ; ALGORITHMS
WOS Research AreaEngineering ; Telecommunications
Funding ProjectNational Key Research and Development Program of China[2017YFA0700304] ; National Natural Science Foundation of China[61673371] ; International Partnership Program of Chinese Academy of Sciences[173321KYSB20180020] ; Liaoning Provincial Natural Science Foundation of China[2019-YQ-09]
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Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/27685
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 Laboratory of Robotics, Chinese Academy of Sciences, Shenyang 110016, China
2.Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China
3.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
4.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
5.University of Chinese Academy of Sciences, Beijing 100049, China
Recommended Citation
GB/T 7714
Gong TR,Yang ZJ,Zheng M,et al. Compressive Subspace Learning With Antenna Cross-Correlations for Wideband Spectrum Sensing[J]. IEEE TRANSACTIONS ON COMMUNICATIONS,2020,68(9):5406-5419.
APA Gong TR,Yang ZJ,Zheng M,Liu ZF,&Wang GS.(2020).Compressive Subspace Learning With Antenna Cross-Correlations for Wideband Spectrum Sensing.IEEE TRANSACTIONS ON COMMUNICATIONS,68(9),5406-5419.
MLA Gong TR,et al."Compressive Subspace Learning With Antenna Cross-Correlations for Wideband Spectrum Sensing".IEEE TRANSACTIONS ON COMMUNICATIONS 68.9(2020):5406-5419.
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