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利用样本特征的盲频谱感知算法 被引量：8

Employing Sample Features for Blind Spectrum Sensing Algorithm
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摘要 为克服噪声不确定度及噪声方差的影响,利用样本特征构造了新的检验统计量,推导了频谱空闲、频谱占用时检验统计量的概率密度函数,提出基于F分布的盲频谱感知算法,但其判决门限受采样点数影响;为此,利用Anderson-Darling准则提出基于F分布的拟合度检验算法。在高斯信道下对两种算法进行了仿真,并与能量检测算法、GOF算法仿真结果比较可知,所提两种算法性能优于噪声方差已知的能量检测算法,并克服能量检测算法、GOF算法受噪声不确定度以及噪声方差影响这一缺陷。 In order to overcome the influence of noise uncertainty and noise variance,a new test statistic is constructed by the use of the sample features. In this paper,the probability density functions（ PDF） of the test statistic under free of frequency channel and busyness of frequency channel are derived and a blind spectrum sensing based on F distribution is proposed. Unfortunately,its threshold is affected by the number of the samples. Hence a goodness of fit（ GOF） based on the F-distribution（ GOF-F） is proposed via Anderson-Darling criterion. Finally,with comparison to the energy detection and normal GOF algorithms,simulations show the performances of proposed algorithms are much better than energy detection that the noise variance had been known,and to overcome the weakness of energy detection and GOF algorithm,which were affected by noise uncertainty and noise variance in the case of Gaussian channel.
作者 叶迎晖 卢光跃 弥寅
机构地区 西安邮电大学无线网络安全技术国家工程实验室
出处 《信号处理》 CSCD 北大核心 2016年第4期444-450,共7页 Journal of Signal Processing
基金 国家自然科学基金项目(61271276 61301091) 陕西省自然科学基金项目(2014JM8299)
关键词 盲频谱感知 Anderson-Darling准则 样本特征 噪声不确定度 噪声方差 blind spectrum sensing Anderson-Darling guideline sample features noise uncertainty noise variance
分类号 TN92 [电子电信—通信与信息系统]
  • 相关文献

参考文献20

  • 1Masonta M T, Mzyece M, Ntlatlapa N. Spectrum deci- sion in cognitive radio networks: A survey [ J ]. IEEE Communications Surveys & Tutorials, 2013, 15 ( 3 ) : 1088-1107.
  • 2Simon Haykin. Cognitive radio: brain-empowered wire-less communications [ J ]. IEEE Journal on Selected Areas in Communications, 2005, 23(2): 201-220.
  • 3Yucek T, Arslan H. A Survey of Spectrum Sensing Mgo- rithms for Cognitive Radio Applications [ J ]. IEEE Commu- nications Surveys & Tutorials, 2009, 11 ( 1 ) : 116-130.
  • 4Sutton P D, Nolan K E, Doyle L E. Cyclostationary sig- natures in practical cognitive radio applications[J]. IEEE Journal on Selected Areas in Communications, 2008, 26 (1) : 13-24.
  • 5Digham F F,Alouini M S,Simon M K. On the Energy Detec- tion of Unknown Signals Over Fading Channels[J].IEEE Transactions on Communications ,2007,55 ( 1 ) :21-24.
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  • 9弥寅,卢光跃.基于特征值极限分布的合作频谱感知算法[J].通信学报,2015,36(1):84-89. 被引量：31
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二级参考文献94

  • 1Haykin S. Cognitive radio: brain-empowered wireless communications [J]. IEEE Journal on Selected Areas in Communications, 2005, 23(2): 201-220.
  • 2Cabric D, Mishra S M, and Brodersen R W. Implementation issues in spectrum sensing for cognitive radios[C]. Proc. of 38th Asilomar Conf. Signals, System, and Computers, Monterey, CA, Nov. 2004: 772-776.
  • 3Digham F, Alouini M, and Simon M. On the energy detection of unknown signals over fading channels[C]. IEEE Int. Conf. Commun., Seattle, Washington, USA, May 2003, Vol. 5: 3575-3579.
  • 4Tang H. Some physical layer issues of wide-band cognitive radio systems[C]. IEEE Int. Symposium on New Frontiers in Dynamic Spectrum Access Networks, Baltimore, Maryland, USA. Nov. 2005: 151-159.
  • 5Sutton P D, Nolan K E, and Doyle L E. Cyclostationary signatures in practical cognitive radio applications[J]. IEEE Journal on Selected Areas in Communications, 2008, 26(1): 13-24.
  • 6Zeng Y and Liang Y C. Maximum-minimum eigenvalue detection for cognitive radio[Cl. The 18th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications. (PIMRC), Athens, Greece, Sep. 2007.
  • 7Penna F and Garello R. Theoretical performance analysis of eigenvalue-based detection, http://arxiv.org/pdf /0907. 1523v2, 2009.9.
  • 8王颖喜,卢光跃.基于空间谱的频谱感知办法[C].第三届全国通信新理论与新技术学术大会,宁波,2009,10.
  • 9Quan z, Cui S, Poor H V, and Sayed A H. Collaborative wideband sensing for cognitive radios[J]. IEEE Signal Processing Magazine, 2008, 25(6): 60-73.
  • 10Sahai A, Hoven N, and Tandra. Some fundamental limits on cognitive radio[C]. Allerton Conf. on Control, Communication, and Computation, Monticello, Oct. 2004. doi=10.1.1.123.5645&zrep.

共引文献76

同被引文献85

  • 1徐家品,杨智.基于随机矩阵特征值比的频谱感知改进算法[J].电波科学学报,2015,30(2):282-288. 被引量：11
  • 2KOLODZY P.Spectrum policy task force report[J].Federal Communications Commission Technology,2002,40(4):147-158.
  • 3ISLAM M H,KOH C L,OH S W,et al.Spectrum survey in Singapore:occupancy measurements and analysis[C]//The 3rd International Conference on Cognitive Radio Oriented Wireless Networks and Communications(CROWNCOM’08),May 15-17,2008,Singapore.New Jersey:IEEE Press,2008:1-7.
  • 4SIMON H.Cognitive radio:brain-empowered wireless communications[J].IEEE Journal on Selected Areas in Communications,2005,23(2):201-220.
  • 5MITOLA J,MAGUIRE G.Cognitive radio:making software radios more personal[J].IEEE Personal Communications,1999,6(4):13-18.
  • 6CABRIC D,MISHRA S M,BRADERSEN R W.Implementation issues in spectrum sensing for cognitive radios[C]//The 38th Asilomar Conference on Signals System and Computers,November 7-10,2004,Monterey,Mexico.New Jersey:IEEE Press,2004:772-776.
  • 7DIGHAM F F,ALOUINI M S,SIMON M K.On the energy detection of unknown signals over fading channels[J].IEEE Transactions on Communications,2007,55(1):21-24.
  • 8TANDRA R,SAHAI A.SNR walls for signal detection[J].IEEE Journal of Selected Topics in Signal Processing,2008,2(1):4-17.
  • 9ZENG Y H,LIANG Y,Maximum-minimum eigenvalue detection for cognitive radio[C]//The 18th Annual IEEE International Symposium on Personal,Indoor and Mobile Radio Communications(PIMRC’07),September 3-7,2007,Greece,Athens.New Jersey:IEEE Press,2007:1-5.
  • 10STEPHENS M A.EDF statistics for goodness of fit and some comparisons[J].Journal of the American Statistical Association,1974,69(347):730-737.

引证文献8

二级引证文献20

信号处理
2016年 第4期

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