期刊文献+

基于频谱数据库的分布式动态频谱接入算法 被引量:2

A Distributed Dynamic Spectrum Access Algorithm based on Spectrum Database
下载PDF
导出
摘要 频谱数据库是一种直接获得频谱信息的方式,针对在复杂多变的网络环境中用户之间缺少信息交互,研究了数据库协助和没有数据库情况下的动态频谱接入算法。一方面,次用户通过历史感知数据估计信道的可用性而进行信道选择,另一方面,次用户通过数据库获得更加可靠的频谱信息从而做出策略。证明了用户之间的博弈是一个超模博弈,通过提出的分布式学习算法能收敛到一个纯策略纳什均衡点。仿真结果表明,提出的联合数据库感知算法和数据库协助算法比依靠感知的结果收敛更快,获得的系统吞吐量接近最大,减少了用户决策的时延,提高了频谱利用率。 Spectrum database could directly acquire work environment Which is lacking information exc spectrum information, and in view of the complex net- hange of between the users, dynamic spectrum access algorithm with and without the aid of database is discussed in this paper. On the one hand, secondary us- ers estimate the availability probability and select channels with historically sensed data, on the other, they acquire farly reliable spectrum information from spectrum database and make proper decision. The game of between the users proves to be a supermodular game, and two distributed leaning algorithms are proposed to achieve the pure strategic NE (Nash Equilibrium). Simulation results show that the proposed database- jointed and database-aided perception algorithms could enjoy fairly fast convergence, nearly maximum throughput, and thus decrease the decision time-delay of decision and improve the spectrum utilization.
出处 《通信技术》 2016年第4期426-430,共5页 Communications Technology
基金 国家自然科学基金(No.61301161 No.61471395) 江苏省自然科学基金(No.BK20141070)~~
关键词 频谱数据库 动态频谱接入 学习算法 纳什均衡 spectrum database dynamic spectrum access learning algorithm NE
  • 相关文献

参考文献14

  • 1Federal Communications Commission. Spectrum PolicyTask Force Report [ R ]. ET Docket No. 02-135,No-vember 2002.
  • 2Shared Spectrum Company, www. sharedspectrum. com.
  • 3ZHAO Q, Sadler B. A Survey of Dynamic Spectrum Ac-cess :Signal Processing,Network,and Regulatory Policy[J]. IEEE Signal Processing,2005,24(3 ) :201-220.
  • 4徐迪.动态频谱接入综述[J].电子科技,2015,28(3):161-164. 被引量:4
  • 5兰昆伟,赵杭生,李湘洋,罗梦麟.认知无线电中基于感知门限的频谱预测研究[J].通信技术,2015,48(2):165-170. 被引量:7
  • 6Yasin Y,GUO Z, WANG X. Sequential Joint SpectrumSensing and Channel Estimation for Dynamic SpectrumAccess [ J ]. IEEE Journal on Selected Areas in Commu-nications ,2014,32( 11) : 2000-2012.
  • 7Marjan Z,Min D, Ali G. Distributed Stochastic Learningfor Dynamic Spectrum Access Adaptive to Primary Net-work Conditions[C]// IEEE SPAWC ,2014:449-4533.
  • 8Murty R,Chandra R,Moscibroda T. Senseless: A Data-base- Driven White Space Network [ J ]. IEEE Transac-tions on Mobile Computing, 2012,11(2) : 189—203.
  • 9LIU Y,YU H,PAN M, et al. Adaptive Channel Accessin Spectrum Database - Driven Cognitive Radio Networks[CJ//IEEE ICC, 2014;4933-4938.
  • 10ZHANG N, LIANG H,CHENG Nan, et al. DynamicSpectrum Access in Multi-Channel Cognitive Radio Net-works [J ]. IEEE Journal on Selected Areas in Commu-nications,2014,32( 11 ) :2053-2064.

二级参考文献24

  • 1Akyildiz I F, I>ee W Y, Vuran M C, et al. NeXt Genera-tion Dynamic Spectrum Access/Cognitive Radio WirelessNetworks: A Survey[ J ]. Computer Networks, 2006, 50(13): 2127-2159.
  • 2Mitola J,Maguire G. Cognitive Radio: Making SoftwareRadios More Personal [ J ]. IEEE Personal Commun,1999, 6(4) ; 12-18.
  • 3Tovfik Y, Arslan H. A Survey of Spectrum Sensing Algo-rithms for Cognitive Radio Applications, IEEE Communi-(*ations surveys & tutorials, 2009( 1 ) : 116—130.
  • 4Lin Y E, Liu K H, Hsieh H Y. On Using Interference-aware Spectrum Sensing for Dynamic Spectrum Access inCognitive Radio Networks[ J ]. Mobile Computing, IEEETransactions on, 2013 ,12 (3 ) :461-474.
  • 5Xing X,Jing T, Chen W,et al. Spectrum Prediction inCognitive Radio Networks [ J ]. Wireless Communications,IEEE, 2013,20(2) :90-96.
  • 6Yarkan S, Arsla H. Binary Time Series Approach toSpectrum Prediction for Cognitive Radio[ C]//VehicularTechnology Conference 2007. 2007 IEEE 66th. IEEE,2007:1563-1567.
  • 7Tumuluru V K, Wang P, Niyato D. A Neural Networkbased Spectrum Prediction Scheme for Cognitive Radio[C ] //Communications ( ICC ) , 2010 IEEE InternationalConference on. IEEE, 2010:1-5.
  • 8LI Y, DONG Y,ZHAING II, et al. Spectrum Usage Pre-diction based on High-order Markov Model for CognitiveRadio Networks[C]//Computer and Information Technol-ogy ( CIT),2010 IEEE 10th International Conference on.IEEE, 2010:2784-2788.
  • 9HUANG P, UU C J, XIAO L, et al. Wireless SpectrumOccupancy Prediction based on Partial Periodic Mining[C ]//Modeling Analysis&Simulation of Computer andTelecommunication Systems ( MASCOTS ) , 2012 IEEE20lh International Symposium on. IKEE,2012:51-58.
  • 10YUAN G,Grammenos K C,Yang Y, et al. Perform-ance Analysis of Selective Opportunistic Access withTraffic Prediction [ J ]. Vehicular Technology, IEEETransactions on. 2010,59(4) : 1949-1959.

共引文献8

同被引文献25

引证文献2

二级引证文献3

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