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Compressive slow-varying wideband power spectrum sensing for cognitive radio

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Abstract

Wideband spectrum sensing is a critical component of a functioning cognitive radio system. Its major challenge is the too high sampling rate requirement. Compressive sensing (CS) promises to be able to deal with it. Nearly all the current CS-based compressive wideband spectrum sensing methods exploit only the frequency sparsity to perform. This paper sets up a new signal model which is sparse in both temporal and frequency domain. Motivated by the achievement of a fast and robust detection of the wideband spectrum change, total variation minimization is incorporated to exploit the temporal and frequency structure information to enhance the sparsity level. As a sparser vector is obtained, the spectrum sensing period would be shortened and sensing accuracy would be enhanced. Both theoretical analysis and numerical experiments demonstrate the performance improvement.

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Acknowledgments

Yipeng Liu was supported by FWO PhD/postdoc grant G.0108.11 (compressed sensing). Qun Wan was supported in part by the National Natural Science Foundation of China under the grant 61172140, and 985 key projects for excellent teaching team supporting (postgraduate) under the grant A1098522-02.

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Authors and Affiliations

  1. Stadius Center for Dynamical Systems, Signal Processingand Data Analytics/iMinds Future Health Department, Department of Electrical Engineering (ESAT), University of Leuven, Kasteelpark Arenberg 10, Box 2446, 3001, Heverlee, Leuven, Belgium

    Yipeng Liu

  2. Electronic Engineering Department, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Qun Wan

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  1. Yipeng Liu
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  2. Qun Wan
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Correspondence to Yipeng Liu.

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Liu, Y., Wan, Q. Compressive slow-varying wideband power spectrum sensing for cognitive radio. Ann. Telecommun. 69, 559–567 (2014). https://doi.org/10.1007/s12243-013-0414-3

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  • DOI: https://doi.org/10.1007/s12243-013-0414-3

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