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基于最小描述长度准则的稀疏多带信号频谱感知算法 被引量：3

Spectrum Sensing Algorithm Based on Minimum Description Length Criterion for Sparse Multiband Signal
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摘要 该文研究稀疏多带信号的频谱感知问题。首先利用欠采样的数据构造自相关矩阵,并对该矩阵进行特征值分解,然后根据最小描述长度(MDL)准则对特征值进行计算,利用结果区分信号子空间和噪声子空间,最后根据子空间的结果求出信号的频率支集,由此提出了一种针对稀疏多带信号的频谱感知算法。由于传统算法需要预先设置门限来区分信号子空间和噪声子空间,所以不合理的门限值会导致算法失效,该文算法不用预先设置门限,具有更好的适应性。仿真实验结果验证了该文算法的有效性。 Spectrum sensing issue for sparse multiband signals is considered in this paper. Firstly, undersampling at sub-Nyquist rate are used to construct correlation matrix, and eigenvalue decomposition is carried out. Then, the method uses Minimum Description Length （MDL） criterion to distinguish between signal subspace and noise subspace and finds the spectral support. A spectrum sensing algorithm for sparse multiband signal is presented. Traditional methods distinguish between signal subspace and noise subspace by setting threshold, and make mistake when the threshold is wrong. Adaptability of this algorithm is better because there is no need to set threshold. The simulation results verify the validity of this algorithm.
作者 杨鹏 柳征 姜文利
机构地区 国防科技大学电子科学与工程学院
出处 《电子与信息学报》 EI CSCD 北大核心 2012年第7期1547-1551,共5页 Journal of Electronics & Information Technology
关键词 信号处理 频谱感知 稀疏多带信号 欠采样 子空间方法 最小描述长度(MDL)准则 Signal processing Spectrum sensing Sparse multiband signal Sub-Nyquist sampling Subspace method Minimum Description Length （MDL） criterion
分类号 TN911.7 [电子电信—通信与信息系统]
  • 相关文献

参考文献10

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二级参考文献15

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共引文献46

同被引文献41

  • 1Hiep V V,Insoo K. A sequential cooperative spectrum sensing scheme based on cognitive user reputation[J].IEEE Transactions on Consumer Electronics,2012,(04):1147-1152.
  • 2Gong Shi-min,Wang Ping,Huang Jian-wei. Robust performance of spectrum sensing in cognitive radio networks[J].IEEE Transactions on Wireless Communications,2013,(05):2217-2227.
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  • 4Haykin S. Cognitive radio:brain-empowed wireless communication[J].IEEE Journal on Selected Areas in Communications,2005,(02):201-220.
  • 5Jing Yuan,Yang Xiao-feng,Ma Li. Blind multiband spectrum sensing in cognitive radio network[A].Yichang,2012.2442-2445.
  • 6Yuced T,Arslan H. A survey of spectrum sensing algorithms for cognitive radio applications[J].IEEE Communications Surveys&Tutorials,2009,(01):116-130.
  • 7Zeng Yong-hong,Liang Ying-chang. Spectrum-sensing algorithms for cognitive radio based on statistical covariances[J].IEEE Transactions on Vehicular Technology,2009,(04):1804-1815.
  • 8Zeng Yong-hong,Liang Ying-chang. Maximum-minimum eigenvalue detection for cognitive radio[A].Athens,2007.1-5.
  • 9Singh A,Bhatnagar M R,Mallik R K. Cooperative spectrum sensing in multiple antenna based cognitive radio network using an improved energy detector[J].IEEE Communications Letters,2012,(01):64-67.
  • 10Tugnait J K. On multiple antenna spectrum sensing under noise variance uncertainty and flat fading[J].IEEE Transactions on Signal Processing,2012,(04):1823-1832.

引证文献3

二级引证文献6

电子与信息学报
2012年 第7期

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