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Communications and Networking. 15th EAI International Conference, ChinaCom 2020, Shanghai, China, November 20-21, 2020, Proceedings

Research Article

Fast Power Spectrum Estimation with Sparse Learning for Wideband Spectrum Sensing

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  • @INPROCEEDINGS{10.1007/978-3-030-67720-6_29,
        author={Shuai Liu and Wen Xiao and Yao Zhang and Jing He and Jixin Wu},
        title={Fast Power Spectrum Estimation with Sparse Learning for Wideband Spectrum Sensing},
        proceedings={Communications and Networking. 15th EAI International Conference, ChinaCom 2020, Shanghai, China, November 20-21, 2020,  Proceedings},
        proceedings_a={CHINACOM},
        year={2021},
        month={2},
        keywords={Wideband spectrum sensing Power spectrum estimation Sparse learning},
        doi={10.1007/978-3-030-67720-6_29}
    }
    
  • Shuai Liu
    Wen Xiao
    Yao Zhang
    Jing He
    Jixin Wu
    Year: 2021
    Fast Power Spectrum Estimation with Sparse Learning for Wideband Spectrum Sensing
    CHINACOM
    Springer
    DOI: 10.1007/978-3-030-67720-6_29
Shuai Liu1,*, Wen Xiao1, Yao Zhang1, Jing He1, Jixin Wu1
  • 1: Xi’an Jiaotong University
*Contact email: sh_liu@mail.xjtu.edu.cn

Abstract

The Compressed Sensing technology in wideband spectrum sensing (WSS) has greatly improved the utilization of spectrum resources. Based on this, we combining sparse learning and fast power spectrum estimation to achieve WSS in this paper. Sparsity adaptive matching pursuit (SAMP) algorithm is exploited to obtain the sparse sample representation for WSS. Then the limi-tations of power spectrum estimation in WSS are considered. To ease the limitations, the computational tasks are decomposed by multiple fast Fourier transforms. Theoretical performance analysis is made to further explain the proposed method. By improving the process of sample collection and power spectrum estimation, the proposed method can effectively achieve the pur-pose of fastly and exactly sensing. The final simulation results are utilized to verify the applicability of the proposed method and its advantages over other methods.

Keywords
Wideband spectrum sensing Power spectrum estimation Sparse learning
Published
2021-02-02
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-030-67720-6_29
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