Total views : 266

Characterization of Primary Users in Cognitive Radio Wireless Networks using Support Vector Machine


  • Department of Electronic Engineering, University Distrital Francisco Jose de Caldas, Bogota, Colombia,South America
  • Department of System Engineering, University Distrital Francisco Jose de Caldas, Bogota, Colombia,South America
  • Department of System Engineering, University Pamplona, Pamplona, Colombia, South America


Objectives: This paper aims to solve a problem of existing research for Cognitive Radio networks Unlicensed (specifically for Wi-Fi with centralized network topology), which aims at the spectral making decision stage to efficiently characterize the behavior of users (called PUs) in order to identify spectral opportunities that can be used beneficially by other wireless applications or users. Methods/ Statistical Analysis: The methodology includes capturing the dynamics of channel usage by a user in the 2.4 GHz band, later to apply a pre-processing of the data and reach an estimate for the level of modeling (based training system) and prediction. Findings: The possible solution that was raised during the development of the research was to use a Support Vector Machine (SVM) to estimate the prediction by classification. Application/Improvements: The results suggest that the use of SVM does not correspond to the methodology of artificial intelligence accurately because the level of delivered estimate is not accurate.


Characterization, Cognitive Radio, Prediction, Primary Users, Spectral Decision, Support Vector Machine, Wi-Fi

Full Text:

 |  (PDF views: 253)


  • Wi-Fi scanner for 802.11 ac networks. Acrylic Wi-Fi home.Apr. 2016. Available from: Crossref
  • Mathworks (support documentation). 2012. Available from: Crossref
  • Chang CC, Lin JL. LIBSVM: A library for Support Vector Machines. ACM Transactions on Intelligent Systems and Technology. 2011; 2(3):1–27. Crossref
  • Akyildiz IF, Lee WY, Vuran MC, Mohanty S. A survey on spectrum management in Cognitive Radio networks. IEEE Communications Magazine. 2008; 46(4):40–8. Crossref
  • Federal Comunications Commission. Notice of proposed rulemaking and order. Mexico D.F: Report ET Docket.2016. Crossref
  • Hernandez C, Paez I, Giral D. AHP-VIKOR model for spectrum handoff in Cognitive Radio networks. Revista Tecnura. 2015; 19(45):29–39. Crossref
  • Granelli G, Sherman S, Mody M . IEEE Standard definitions and concepts for dynamic spectrum access: Terminology relating to emerging wireless networks, system functionality and spectrum management. IEEE Standard. 2010; 20(2):1–9.
  • Sahai A, Hoven N, Tandra R. Some fundamental limits on cognitive radio. Department Electrical Engineering and Computer Science. University of California. Apr. 2005; 1:464–9.
  • Lopez D, Trujillo E, Gualdron O. Elementos fundamentales que componen la radio cognitiva y asignacion de bandas espectrales.Revista Informacion Tecnologica. 2015; 26(1):23–40. Crossref
  • Saleem Y, Rehmani MH. Primary radio user activity models for Cognitive Radio networks: A survey. Elsevier. Journal of Network and Computer Applications. 2014; 43(1):1–16.Crossref
  • Meerschaert MM. Mathematical modeling. Academic press is an imprint of Elsevier; Fourth ed. 2013. p. 21–50.
  • Sharma H, Kumar K. Primary user emulation attack analysis on cognitive radio. Indian Journal of Science and Technology.2016; 9(14):1–6. Crossref
  • PCA (Principal-component-analysis). 2016. Available from: Crossref
  • Mathworks. MATLAB mex support for visual studio (and mbuild). 2013. Available from: Crossref
  • NTNU, Department of mathematical sciences. 2015. Available from: Crossref
  • Xing X, Jing T, Cheng W, Huo Y, Cheng X. Spectrum prediction in Cognitive Radio networks. IEEE in Wireless Communications. 2013; 20(2):90–6. Crossref


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.