Scheduling of Collaborative Sequential Compressed Sensing Over Wide Spectrum Band

Zhao, Jie; Liu, Qiang; Wang, Xin; Mao, Shiwen

doi:10.1109/TNET.2017.2787647

Title: Scheduling of Collaborative Sequential Compressed Sensing Over Wide Spectrum Band

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Abstract

The cognitive radio (CR) technology holds promise to significantly increase spectrum availability and wireless network capacity. With more spectrum bands opened up for CR use, it is critical yet challenging to perform efficient wideband sensing. In this work, we propose an integrated sequential wideband sensing scheduling framework that concurrently exploits sequential detection and compressed sensing (CS) techniques for more accurate and lower-cost spectrum sensing. First, to ensure more timely detection without incurring high overhead involved in periodic recovery of CS signals, we propose smart scheduling of a CS-based sequential wideband detection scheme to effectively detect the PU activities in the wideband of interest. Second, to further help users under severe channel conditions identify the occupied sub-channels, we develop two collaborative strategies, namely, joint reconstruction of the signals among neighboring users and wideband sensing-map fusion. Third, to achieve robust wideband sensing, we propose the use of anomaly detection in our framework. Lastly, extensive simulations demonstrate that our approach outperforms peer schemes significantly in terms of sensing delay, accuracy and overhead.

Authors:

^[1];

^[2]; Wang, Xin ^[1];

^[3]

Stony Brook Univ., NY (United States). Department of Electrical and Computer Engineering
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Computational Sciences and Engineering Division
Auburn Univ., AL (United States). Department of Electrical and Computer Engineering

Publication Date:: 2018-01-17

Research Org.:: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Sponsoring Org.:: USDOE

OSTI Identifier:: 1468109

Grant/Contract Number:: AC05-00OR22725

Resource Type:: Accepted Manuscript

Journal Name:: IEEE/ACM Transactions on Networking

Additional Journal Information:: Journal Volume: 26; Journal Issue: 1; Journal ID: ISSN 1063-6692

Publisher:: IEEE - ACM

Country of Publication:: United States

Language:: English

Subject:: 97 MATHEMATICS AND COMPUTING; 42 ENGINEERING; Cognitive radio; sequential detection; wideband sensing; compressed sensing; cooperative sensing

Citation Formats


                    Zhao, Jie, Liu, Qiang, Wang, Xin, and Mao, Shiwen. Scheduling of Collaborative Sequential Compressed Sensing Over Wide Spectrum Band.  United States: N. p., 2018. 
Web.  doi:10.1109/TNET.2017.2787647.

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                    Zhao, Jie, Liu, Qiang, Wang, Xin, & Mao, Shiwen. Scheduling of Collaborative Sequential Compressed Sensing Over Wide Spectrum Band.  United States.  https://doi.org/10.1109/TNET.2017.2787647

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                    Zhao, Jie, Liu, Qiang, Wang, Xin, and Mao, Shiwen. Wed .  
"Scheduling of Collaborative Sequential Compressed Sensing Over Wide Spectrum Band".  United States.  https://doi.org/10.1109/TNET.2017.2787647.  https://www.osti.gov/servlets/purl/1468109.

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@article{osti_1468109,

  title        = {Scheduling of Collaborative Sequential Compressed Sensing Over Wide Spectrum Band},

  author       = {Zhao, Jie and Liu, Qiang and Wang, Xin and Mao, Shiwen},

  abstractNote = {The cognitive radio (CR) technology holds promise to significantly increase spectrum availability and wireless network capacity. With more spectrum bands opened up for CR use, it is critical yet challenging to perform efficient wideband sensing. In this work, we propose an integrated sequential wideband sensing scheduling framework that concurrently exploits sequential detection and compressed sensing (CS) techniques for more accurate and lower-cost spectrum sensing. First, to ensure more timely detection without incurring high overhead involved in periodic recovery of CS signals, we propose smart scheduling of a CS-based sequential wideband detection scheme to effectively detect the PU activities in the wideband of interest. Second, to further help users under severe channel conditions identify the occupied sub-channels, we develop two collaborative strategies, namely, joint reconstruction of the signals among neighboring users and wideband sensing-map fusion. Third, to achieve robust wideband sensing, we propose the use of anomaly detection in our framework. Lastly, extensive simulations demonstrate that our approach outperforms peer schemes significantly in terms of sensing delay, accuracy and overhead.},

  doi          = {10.1109/TNET.2017.2787647},

  journal      = {IEEE/ACM Transactions on Networking},

  number       = 1,

  volume       = 26,

  place        = {United States},

  year         = {2018},

  month        = {1}

}

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