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Wideband Power Spectrum Sensing for Cognitive Radios Based on Sub-Nyquist Sampling

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Abstract

In a cognitive radio system, efficient wideband spectrum estimation is a basic component of dynamic spectrum access. The systems high sampling rate is the main challenge in the frontend. In this paper, wideband power spectrum sensing is studied based on sub-Nyquist sampling instead of signal recovery. Compared to other spectrum sensing methods based on sub-Nyquist sampling, the proposed scheme is suitable for both sparse and nonsparse signals. A low complexity, adaptive resolution frequency averaging scheme is proposed to exploit the cross-power spectrum between the outputs of different channels. Spectrum reconstruction presents only a simple least square without any sparse constraint. The normalized mean square error is computed to demonstrate estimation performance.

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Authors and Affiliations

  1. Key Laboratory of Wireless Sensor Networks, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Science, Shanghai, China

    Lebing Pan, Shiliang Xiao & Xiaobing Yuan

  2. University of Chinese Academy of Sciences, Beijing, China

    Lebing Pan & Shiliang Xiao

Authors
  1. Lebing Pan
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  2. Shiliang Xiao
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  3. Xiaobing Yuan
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Correspondence to Lebing Pan.

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Pan, L., Xiao, S. & Yuan, X. Wideband Power Spectrum Sensing for Cognitive Radios Based on Sub-Nyquist Sampling. Wireless Pers Commun 84, 919–933 (2015). https://doi.org/10.1007/s11277-015-2668-8

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  • DOI: https://doi.org/10.1007/s11277-015-2668-8

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