东北大学学报（自然科学版）

东北大学学报:自然科学版 ›› 2018, Vol. 39 ›› Issue (1): 31-34.DOI: 10.12068/j.issn.1005-3026.2018.01.007

• 信息与控制 • 上一篇    下一篇

基于动态组稀疏重构的频谱感知算法

刘福来1,2， 刘蕾2， 杜瑞燕1,2， 张淼2   

  1. (1. 东北大学秦皇岛分校 计算机与通信工程学院， 河北 秦皇岛066004; 2. 东北大学 计算机科学与工程学院， 辽宁 沈阳110169)
  • 收稿日期:2016-07-25 修回日期:2016-07-25 出版日期:2018-01-15 发布日期:2018-01-31
  • 通讯作者: 刘福来
  • 作者简介:刘福来( 1975－)，男，河北唐山人，东北大学教授，博士生导师.
  • 基金资助:
    新世纪优秀人才支持计划项目(NCET-13-0105)； 河北省高校百名优秀创新人才支持计划项目(BR2-259)； 河北省自然科学基金资助项目(F2016501139) ； 中国高等教育博士研究生专项科研基金资助项目(20130042110003)； 中央高校基本科研业务费专项资金资助项目(N142302001).

Spectrum Sensing Algorithm Based on Dynamic Group Sparsity Reconstruction

LIU Fu-lai1,2， LIU Lei2， DU Rui-yan1,2， ZHANG Miao2   

  1. 1. School of Computer and Communication Engineering， Northeastern University at Qinhuangdao，Qinhuangdao 066004， China; 2. School of Computer Science & Engineering， Northeastern University， Shenyang 110169， China.
  • Received:2016-07-25 Revised:2016-07-25 Online:2018-01-15 Published:2018-01-31
  • Contact: LIU Lei
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摘要： 针对认知无线电网络中宽带频谱感知问题，提出了一种基于主用户信号频谱结构的频谱感知算法，简称为DGS-SS算法.该算法首先利用压缩感知理论对信号进行欠采样，然后利用主用户信号频谱的组稀疏结构修正重构过程中的频谱和残差支撑集，从而能够加快重构主用户信号频谱的收敛速度，而且也能够提高主用户信号频谱的重构精度，最后利用重构信号频谱给出频谱空穴的有效检测.仿真结果表明，所提算法不仅能在低压缩比下精确重建信号频谱，而且对噪声变化具有更强的鲁棒性，从而有效地提高了频谱感知性能.

关键词: 认知无线电, 频谱感知, 压缩感知, 动态组稀疏, 主用户信号重构

Abstract: To solve the problem of wideband spectrum sensing in cognitive radio networks， a spectrum sensing algorithm based on the spectrum structure of primary user signals was proposed， which is called DGS-SS algorithm. Firstly， compressed sensing theory was applied to signal acquisition to achieve a sub-Nyquist rate. Secondly， the group sparsity structure of primary user spectrum was used to modify the spectrum and residual support set during the reconstruction process， which can speed up the convergence and improve the accuracy of the reconstruction of primary user spectrum. Finally， effective detection of spectrum holes was given by the reconstructed signal spectrum. Simulation results show that the proposed algorithm can accurately reconstruct the spectrum at low compression ratio and have stronger robustness to noise variation， which makes the spectrum sensing performance significantly improved.

Key words: cognitive radio, spectrum sensing, compressed sensing, dynamic group sparsity, primary user signal reconstruction

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