期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

基于二项分布的快速盲频谱感知算法 被引量：1

Fast and blind spectrum sensing based on binomial distribution
下载PDF
导出
摘要 基于特征函数的盲频谱感知算法(CAD)检测性能较好但运算量大。利用样本特征构造了新的检验统计量,推导了频谱空闲时检验统计量的概率密度函数和判决门限,分析了所提算法的检测性能和计算量,从而提出认知无线电中基于二项分布的快速盲频谱感知算法。理论分析和仿真表明,所提算法与CAD算法检测性能相当,同时运算量明显低于CAD算法。 The performance of the existing blind spectrum sensing algorithm based on characteristic function and Anderson-Darling test（CAD） is excellent, however, with heavily computation. In this paper, the sample feature is employed as the test statistic to sense the available spectrum for the cognitive users and a blind and fast spectrum sensing based on binomial distribution is pro- posed. The probability density functions （PDF） of test statistic under free of frequency channel is derived and then theoretical threshold is given. Finally, with comparison to CAD algorithm, analysis and numerical simulations show the proposed algorithm has almost comparable performances and low computation apparently.
作者 苏杭 叶迎晖 卢光跃
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 《电视技术》 北大核心 2016年第4期96-100,共5页 Video Engineering
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 运算量 快速盲频谱感知 二项分布 样本特征 computation fast and blind spectrum sensing binomial distribution sample feature
分类号 TP391 [自动化与计算机技术—计算机应用技术]
  • 相关文献

参考文献15

  • 1张乃谦,金立标,柴剑平.认知无线电技术在广电中的应用[J].电视技术,2012,36(12):22-24. 被引量：5
  • 2MASONTA M T, MZYECE M, NTLATLAPA N. Spectrum decision in cognitive radio networks: A survey [ J ]. IEEE communications surveys & tutorials, 2013, 15 (3): 1088-1107.
  • 3YUCEK T,ARSLAN H. A survey of spectrum sensing algo- rithms for cognitive radio applications [ J]. IEEE communi- cations surveys & tutorials,2009,11 ( 1 ) : 116-130.
  • 4SUTrON P D, NOLAN K E, DOYLE L E. Cyclostationary signatures in practical cognitive radio applications [ J 1. IEEE journal on selected areas in communications,2008,26 (1) : 13-24.
  • 5TANDRA R,SAHAI A. SNR walls for signal detection [ J 1- IEEE journal of selected topics in signal processing, 2008, 2(1) :4-17.
  • 6JAIN S A, DESHMUKH M M. Performance analysis of en- ergy and eigenvalue based detection for spectrum sensing in Cognitive Radio network [ C ]//Proc. International Confer- ence on Pervasive Computing. Pune: [ s. n. ] ,2015:1-5.
  • 7王颖喜,卢光跃.基于最大最小特征值之差的频谱感知技术研究[J].电子与信息学报,2010,32(11):2571-2575. 被引量：47
  • 8ZHANG P, QIU R,GUO N. Demonstration of spectrum sens- ing with blindly learned feature [ J ]. IEEE communication letters, 2011,15 (5) :548-550.
  • 9孙宇,卢光跃,弥寅.子空间投影的频谱感知算法研究[J].信号处理,2015,31(4):483-489. 被引量：10
  • 10卢光跃,弥寅,包志强,冯景瑜.基于特征结构的频谱感知算法[J].西安邮电大学学报,2014,19(2):1-12. 被引量：23

二级参考文献121

  • 1王军,李少谦.认知无线电:原理、技术与发展趋势[J].中兴通讯技术,2007,13(3):1-4. 被引量：39
  • 2Haykin S. Cognitive radio: brain-empowered wireless communications [J]. IEEE Journal on Selected Areas in Communications, 2005, 23(2): 201-220.
  • 3Cabric 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.
  • 4Digham 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.
  • 5Tang 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.
  • 6Sutton 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.
  • 7Zeng 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.
  • 8Penna F and Garello R. Theoretical performance analysis of eigenvalue-based detection, http://arxiv.org/pdf /0907. 1523v2, 2009.9.
  • 9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.
  • 10Quan 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.

共引文献68

同被引文献37

  • 1KOLODZY P.Spectrum policy task force report[J].Federal Communications Commission Technology,2002,40(4):147-158.
  • 2ISLAM 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.
  • 3SIMON H.Cognitive radio:brain-empowered wireless communications[J].IEEE Journal on Selected Areas in Communications,2005,23(2):201-220.
  • 4MITOLA J,MAGUIRE G.Cognitive radio:making software radios more personal[J].IEEE Personal Communications,1999,6(4):13-18.
  • 5CABRIC 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.
  • 6DIGHAM 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.
  • 7TANDRA R,SAHAI A.SNR walls for signal detection[J].IEEE Journal of Selected Topics in Signal Processing,2008,2(1):4-17.
  • 8ZENG 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.
  • 9STEPHENS M A.EDF statistics for goodness of fit and some comparisons[J].Journal of the American Statistical Association,1974,69(347):730-737.
  • 10ANDERSON T W,DARLING D A.A test of goodness of fit[J].Journal of the American Statistical Association,1954,49(268):765-769.

引证文献1

二级引证文献4

电视技术
2016年 第4期

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部