Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/584906
Title: Data-Assisted Low Complexity Compressive Spectrum Sensing on Real-Time Signals Under Sub-Nyquist Rate
Authors: Zhijin Qin;Yue Gao;Clive G. Parini
subject: wideband spectrum sensing|TV white space|Compressive spectrum sensing|geo-location database
Year: 2016
Publisher: IEEE
Abstract: In this paper, we present a novel hybrid framework combining compressive spectrum sensing with geo-location database to find spectrum holes in a decentralized cognitive radio. In the hybrid framework, a geo-location database algorithm is proposed to be stored locally at secondary users (SUs) to remove the extra transmission link to a centralized remote geo-location database. Specifically, by utilizing the output of the locally stored geo-location database algorithm, a data-assisted noniteratively reweighted least squares (DNRLS)-based compressive spectrum sensing algorithm is proposed to improve detection performance under sub-Nyquist sampling rates for wideband spectrum sensing, and to reduce the computational complexity of signal recovery. In addition, an efficient method for the calculation of maximum allowable equivalent isotropic radiated power in TV white space (TVWS) is also designed to further support SUs. The convergence and complexity of the proposed DNRLS algorithm are analyzed theoretically. Furthermore, the proposed framework is pioneered on real-time “from air” signals and data after having been validated by simulated signals and data in TVWS.
Description: 
URI: http://localhost/handle/Hannan/166073
http://localhost/handle/Hannan/584906
ISSN: 1536-1276
volume: 15
issue: 2
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7287796.pdf1.06 MBAdobe PDFThumbnail
Preview File
Title: Data-Assisted Low Complexity Compressive Spectrum Sensing on Real-Time Signals Under Sub-Nyquist Rate
Authors: Zhijin Qin;Yue Gao;Clive G. Parini
subject: wideband spectrum sensing|TV white space|Compressive spectrum sensing|geo-location database
Year: 2016
Publisher: IEEE
Abstract: In this paper, we present a novel hybrid framework combining compressive spectrum sensing with geo-location database to find spectrum holes in a decentralized cognitive radio. In the hybrid framework, a geo-location database algorithm is proposed to be stored locally at secondary users (SUs) to remove the extra transmission link to a centralized remote geo-location database. Specifically, by utilizing the output of the locally stored geo-location database algorithm, a data-assisted noniteratively reweighted least squares (DNRLS)-based compressive spectrum sensing algorithm is proposed to improve detection performance under sub-Nyquist sampling rates for wideband spectrum sensing, and to reduce the computational complexity of signal recovery. In addition, an efficient method for the calculation of maximum allowable equivalent isotropic radiated power in TV white space (TVWS) is also designed to further support SUs. The convergence and complexity of the proposed DNRLS algorithm are analyzed theoretically. Furthermore, the proposed framework is pioneered on real-time “from air” signals and data after having been validated by simulated signals and data in TVWS.
Description: 
URI: http://localhost/handle/Hannan/166073
http://localhost/handle/Hannan/584906
ISSN: 1536-1276
volume: 15
issue: 2
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7287796.pdf1.06 MBAdobe PDFThumbnail
Preview File
Title: Data-Assisted Low Complexity Compressive Spectrum Sensing on Real-Time Signals Under Sub-Nyquist Rate
Authors: Zhijin Qin;Yue Gao;Clive G. Parini
subject: wideband spectrum sensing|TV white space|Compressive spectrum sensing|geo-location database
Year: 2016
Publisher: IEEE
Abstract: In this paper, we present a novel hybrid framework combining compressive spectrum sensing with geo-location database to find spectrum holes in a decentralized cognitive radio. In the hybrid framework, a geo-location database algorithm is proposed to be stored locally at secondary users (SUs) to remove the extra transmission link to a centralized remote geo-location database. Specifically, by utilizing the output of the locally stored geo-location database algorithm, a data-assisted noniteratively reweighted least squares (DNRLS)-based compressive spectrum sensing algorithm is proposed to improve detection performance under sub-Nyquist sampling rates for wideband spectrum sensing, and to reduce the computational complexity of signal recovery. In addition, an efficient method for the calculation of maximum allowable equivalent isotropic radiated power in TV white space (TVWS) is also designed to further support SUs. The convergence and complexity of the proposed DNRLS algorithm are analyzed theoretically. Furthermore, the proposed framework is pioneered on real-time “from air” signals and data after having been validated by simulated signals and data in TVWS.
Description: 
URI: http://localhost/handle/Hannan/166073
http://localhost/handle/Hannan/584906
ISSN: 1536-1276
volume: 15
issue: 2
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7287796.pdf1.06 MBAdobe PDFThumbnail
Preview File