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Data-Assisted Low Complexity Compressive Spectrum Sensing on Real-Time Signals under Sub-Nyquist Rate

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Data-Assisted Low Complexity Compressive Spectrum Sensing on Real-Time Signals under Sub-Nyquist Rate. / Qin, Zhijin; Gao, Yue; Parini, Clive.
In: IEEE Transactions on Wireless Communications, Vol. 15, No. 2, 02.2016, p. 1174-1185.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Qin, Z, Gao, Y & Parini, C 2016, 'Data-Assisted Low Complexity Compressive Spectrum Sensing on Real-Time Signals under Sub-Nyquist Rate', IEEE Transactions on Wireless Communications, vol. 15, no. 2, pp. 1174-1185. https://doi.org/10.1109/TWC.2015.2485992

APA

Qin, Z., Gao, Y., & Parini, C. (2016). Data-Assisted Low Complexity Compressive Spectrum Sensing on Real-Time Signals under Sub-Nyquist Rate. IEEE Transactions on Wireless Communications, 15(2), 1174-1185. https://doi.org/10.1109/TWC.2015.2485992

Vancouver

Qin Z, Gao Y, Parini C. Data-Assisted Low Complexity Compressive Spectrum Sensing on Real-Time Signals under Sub-Nyquist Rate. IEEE Transactions on Wireless Communications. 2016 Feb;15(2):1174-1185. Epub 2015 Oct 2. doi: 10.1109/TWC.2015.2485992

Author

Qin, Zhijin ; Gao, Yue ; Parini, Clive. / Data-Assisted Low Complexity Compressive Spectrum Sensing on Real-Time Signals under Sub-Nyquist Rate. In: IEEE Transactions on Wireless Communications. 2016 ; Vol. 15, No. 2. pp. 1174-1185.

Bibtex

@article{72750b3f9bd842cfa49d937f93d3d224,
title = "Data-Assisted Low Complexity Compressive Spectrum Sensing on Real-Time Signals under Sub-Nyquist Rate",
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.",
author = "Zhijin Qin and Yue Gao and Clive Parini",
year = "2016",
month = feb,
doi = "10.1109/TWC.2015.2485992",
language = "English",
volume = "15",
pages = "1174--1185",
journal = "IEEE Transactions on Wireless Communications",
issn = "1536-1276",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "2",

}

RIS

TY - JOUR

T1 - Data-Assisted Low Complexity Compressive Spectrum Sensing on Real-Time Signals under Sub-Nyquist Rate

AU - Qin, Zhijin

AU - Gao, Yue

AU - Parini, Clive

PY - 2016/2

Y1 - 2016/2

N2 - 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.

AB - 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.

U2 - 10.1109/TWC.2015.2485992

DO - 10.1109/TWC.2015.2485992

M3 - Journal article

VL - 15

SP - 1174

EP - 1185

JO - IEEE Transactions on Wireless Communications

JF - IEEE Transactions on Wireless Communications

SN - 1536-1276

IS - 2

ER -