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Distributed Compressive Sensing Based Spectrum Sensing Method

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Machine Learning and Intelligent Communications (MLICOM 2017)

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Abstract

For multi-antenna system, the difficulties of preforming spectrum sensing are high sampling rate and hardware cost. To alleviate these problems, we propose a novel utilization of distributed compressive sensing for the multi-antenna case. The multi-antenna signals first are sampled in terms of distributed compressive sensing, and then the time-domain signals are reconstructed. Finally, spectrum sensing is performed with help of energy-based sensing method. To evaluate the proposed method, we do the corresponding simulations. The simulation results proves the proposed method.

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Acknowledgments

This work is supported by National Natural Science Foundation of China (NSFC) (61671176).

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Authors and Affiliations

  1. School of Computer and Information Engineering, Harbin University of Commerce, Harbin, China

    Yanping Chen

  2. Department of Communications Engineering, Harbin Institute of Technology, Harbin, China

    Yulong Gao & Yongkui Ma

Authors
  1. Yanping Chen
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  2. Yulong Gao
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  3. Yongkui Ma
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Corresponding author

Correspondence to Yulong Gao .

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Editors and Affiliations

  1. Harbin Institute of Technology, Harbin, Heilongjiang, China

    Xuemai Gu

  2. Harbin Institute of Technology, Weihai, Heilongjiang, China

    Gongliang Liu

  3. Shandong University, Weihai, Heilongjiang, China

    Bo Li

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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Chen, Y., Gao, Y., Ma, Y. (2018). Distributed Compressive Sensing Based Spectrum Sensing Method. In: Gu, X., Liu, G., Li, B. (eds) Machine Learning and Intelligent Communications. MLICOM 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 226. Springer, Cham. https://doi.org/10.1007/978-3-319-73564-1_24

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  • DOI: https://doi.org/10.1007/978-3-319-73564-1_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-73563-4

  • Online ISBN: 978-3-319-73564-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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