Machine Learning and Intelligent Communications. Second International Conference, MLICOM 2017, Weihai, China, August 5-6, 2017, Proceedings, Part I

Research Article

Distributed Compressive Sensing Based Spectrum Sensing Method

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  • @INPROCEEDINGS{10.1007/978-3-319-73564-1_24,
        author={Yanping Chen and Yulong Gao and Yongkui Ma},
        title={Distributed Compressive Sensing Based Spectrum Sensing Method},
        proceedings={Machine Learning and Intelligent Communications. Second International Conference, MLICOM 2017, Weihai, China, August 5-6, 2017, Proceedings, Part I},
        proceedings_a={MLICOM},
        year={2018},
        month={2},
        keywords={Distributed compressive sensing Spectrum sensing Joint sparse model Time-domain detection},
        doi={10.1007/978-3-319-73564-1_24}
    }
    
  • Yanping Chen
    Yulong Gao
    Yongkui Ma
    Year: 2018
    Distributed Compressive Sensing Based Spectrum Sensing Method
    MLICOM
    Springer
    DOI: 10.1007/978-3-319-73564-1_24
Yanping Chen1,*, Yulong Gao2,*, Yongkui Ma2,*
  • 1: Harbin University of Commerce
  • 2: Harbin Institute of Technology
*Contact email: yanping1009@163.com, ylgao@hit.edu.cn, yk_ma@hit.edu.cn

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.