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Nonparametric Multicycle Spectrum Sensing Method by Segmented Data Processing for Cognitive Radio

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Abstract

This paper proposes a novel multicycle spectrum sensing method. The method first divides the received data samples into several segments, and then gets cyclic autocorrelation estimates from these segments, constructs a test statistic from these estimates, and finally makes an F-test to determine whether there is a primary signal or not. The method can use multiple cyclic frequencies simultaneously; hence, it is a multicycle detector. The method is also nonparametric, and thus it is robust to noise uncertainty. Compared with existing cyclic spectrum sensing methods, the proposed method can reduce computational complexity significantly at the cost of a little performance loss. Simulation results are given to show the validity and the superiority of the proposed method.

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Acknowledgements

This work was supported in part by the Natural Science Foundation of China under Grant No. 61205049 and in part by the Natural Science Foundation of Fujian Province under Grant No. 2012J01269.

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Authors and Affiliations

  1. College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, 350108, Fujian Province, China

    Jun Wang

  2. Fujian Integrated Circuit Design Center, Fuzhou, 350003, Fujian Province, China

    Jun Wang, Jiwei Huang & Minghui Fan

  3. College of Physics and Information Engineering, Fuzhou University, Fuzhou, 350108, Fujian Province, China

    Jiwei Huang & Minghui Fan

  4. Key Laboratory of Electronic Restriction, Hefei, 230037, Anhui Province, China

    Hongjun Wang

Authors
  1. Jun Wang
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  2. Jiwei Huang
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  3. Minghui Fan
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  4. Hongjun Wang
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Correspondence to Jun Wang.

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Wang, J., Huang, J., Fan, M. et al. Nonparametric Multicycle Spectrum Sensing Method by Segmented Data Processing for Cognitive Radio. Circuits Syst Signal Process 33, 299–307 (2014). https://doi.org/10.1007/s00034-013-9628-x

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  • DOI: https://doi.org/10.1007/s00034-013-9628-x

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