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Spectrum Sensing Based on Fractional Lower Order Power Spectral Density in Alpha-Stable Noise Environments

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Communications, Signal Processing, and Systems (CSPS 2018)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 515))

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Abstract

In view of the fact that the alpha distribution does not possess two order moment and power spectrum, the detection performance of the traditional detectors, such as energy detector (ED) and power spectral density (PSD) detector, will be degraded or even failed when the background noise be modeled as alpha-stable distribution in CR system. This paper presents a novel spectrum-sensing scheme based on fractional lower order statistics power spectral density (FPSD). The proposed algorithm, combining pseudo-PSD and Fourier transform (FT), calculates the FPSD of the received signal to determine whether primary user (PU) is present or absent. Via the numerous simulations, the performance of the FPSD versus the characteristic exponents \( \alpha \), the moment \( p \), and generalized signal-to-noise (GSNR) of the noise has been studied. Simulations show that the proposed FPSD detector has greater performance than ED and PSD in alpha-stable noise environment. In addition, the new detector, as a blind detector, has high probability detection without the prior knowledge of PU signal and noise.

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Acknowledgment

This work was supported by the National Natural Science Foundation of China under Grant 61501223, 61501224.

Author information

Authors and Affiliations

  1. College of Computer Science and Technology, Nanjing Tech University, Nanjing, JiangSu Province, China

    Xiaomei Zhu, Yongjian Song & Yaping Bao

  2. Department of Information and Computing Science, Nanjing Tech University, Nanjing, JiangSu Province, China

    Tianjing Wang

Authors
  1. Xiaomei Zhu
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  2. Yongjian Song
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  3. Tianjing Wang
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  4. Yaping Bao
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Corresponding author

Correspondence to Yongjian Song .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, USA

    Qilian Liang

  2. School of Information and Communication Engineering, Dalian University of Technology, Dalian, Liaoning, China

    Xin Liu

  3. School of Information Science and Technology, Dalian Maritime University, Dalian, China

    Zhenyu Na

  4. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Wei Wang

  5. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Jiasong Mu

  6. College of Physical and Electronic Information, Tianjin Normal University, Tianjin, China

    Baoju Zhang

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Zhu, X., Song, Y., Wang, T., Bao, Y. (2019). Spectrum Sensing Based on Fractional Lower Order Power Spectral Density in Alpha-Stable Noise Environments. In: Liang, Q., Liu, X., Na, Z., Wang, W., Mu, J., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2018. Lecture Notes in Electrical Engineering, vol 515. Springer, Singapore. https://doi.org/10.1007/978-981-13-6264-4_127

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  • DOI: https://doi.org/10.1007/978-981-13-6264-4_127

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-6263-7

  • Online ISBN: 978-981-13-6264-4

  • eBook Packages: EngineeringEngineering (R0)

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