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Wideband compressed spectrum sensing based on modified sparsity adaptive matching pursuit algorithm

Jiao Chuanhai,  Li Yongcheng

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Citation: Jiao Chuanhai, Li Yongcheng. Wideband compressed spectrum sensing based on modified sparsity adaptive matching pursuit algorithm[J]. High Power Laser and Particle Beams, 2018, 30: 033203. doi: 10.11884/HPLPB201830.170395
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引用本文: 焦传海, 李永成. 基于改进的稀疏度自适应匹配追踪算法的宽带压缩频谱感知[J]. 强激光与粒子束, 2018, 30: 033203. doi: 10.11884/HPLPB201830.170395
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Wideband compressed spectrum sensing based on modified sparsity adaptive matching pursuit algorithm

  • 1.

    State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, Luoyang 473000, China

  • 2.

    Army Artillery and Air Defense Academy, Hefei 230031, China

Funds: 

State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System Open Foundation CEMEE2015Z0203B

Natural Science Foundation of Anhui Province 1608085QF143

More Information
    Author Bio:

    Jiao Chuanhai(1983—), male, PhD, engaged in wireless signal processing and cognitive radio research; jiao_chuanhai@126.com

基于改进的稀疏度自适应匹配追踪算法的宽带压缩频谱感知

doi: 10.11884/HPLPB201830.170395
  • 1.

    电子信息系统复杂电磁环境效应国家重点实验室, 河南 洛阳 471000

  • 2.

    陆军炮兵防空兵学院, 合肥 230031

基金项目: 

State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System Open Foundation CEMEE2015Z0203B

Natural Science Foundation of Anhui Province 1608085QF143

详细信息

  • 中图分类号: TN911

摘要

    摘要
  • Abstract: Traditional spectrum sensing based on compressed sensing assumes that the sparsity is known, while actually, it is unknown. To solve the problem, we proposed a modified sparsity adaptive matching pursuit (MSAMP) algorithm. The MSAMP algorithm pre-estimates the value of sparsity in the process of choosing the support set. A cooperative wideband spectrum compressed sensing method is developed based on the MSAMP algorithm and sequential compression inspection technique. Theoretical analysis and simulation results show that the method can enhance the spectrum sensing capability without a priori knowledge of sparsity.
    摘要: 针对在实际宽带压缩频谱感知中难以预先获知宽带频谱稀疏度的问题，提出一种改进的稀疏度自适应匹配追踪(modified sparsity adaptive matching pursuit, MSAMP)算法，该算法在支撑集选择过程中对稀疏度进行了预估计。结合序贯压缩检测技术，给出了一种基于该算法的多认知用户合作场景下的宽带压缩频谱感知方法，理论分析和实验仿真结果表明，该方法可在频谱稀疏度先验知识缺少的情况下，有效提高宽带频谱感知性能。
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  • Figure  1.  Scene model of central cooperative sensing

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    Figure  2.  Framework scheme of proposed method

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    Figure  3.  ROC curves of different methods with Kr=Ks=16

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    Figure  4.  ROC curves of different methods with Kr=32, Ks=16

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    Figure  5.  Performance of different methods varies with RSN

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    Figure  6.  Average running time of different algorithms varies with sparsity ratio

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    • 参考文献(19)
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    [14] Wang Jian, Kwon S, Li Ping, et al. Recovery of sparsity signals via generalized orthogonal matching pursuit: A new analysis[J]. IEEE Trans Signal Processing, 2016, 64 (4): 1076-1089. doi: 10.1109/TSP.2015.2498132
    [15] Malioutov D M, Sanghavi S R, Willsky A S. Sequential compressed sensing[J]. IEEE Journal of Selected Topics in Signal Processing, 2010, 4 (2): 435-444. doi: 10.1109/JSTSP.2009.2038211
    [16] Zhang Henglong, Song Xiaoqin, Zhu Yonggang, et al. Spectrum sensing algorithm based on variable step-size sequential compressed sampling[J]. Journal of Data Acquisition and Processing, 2015, 30 (4): 839-847.
    [17] Baraniuk R G, Cevher V, Duarte M F, et al. Model-based compressive sensing[J]. IEEE Trans Information Theory, 2010, 56 (4): 1982-2001. doi: 10.1109/TIT.2010.2040894
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出版历程
  • 收稿日期:  2017-10-10
  • 修回日期:  2017-11-13
  • 刊出日期:  2018-03-15

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