Spectrum Usage Models for the Analysis, Design and Simulation of Cognitive Radio Networks

  • Miguel López-Benítez
  • Fernando Casadevall
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 116)


This chapter presents a comprehensive set of spectrum occupancy models specifically envisaged for the analysis, design and simulation of cognitive radio systems. The presented models have been proven to accurately capture and reproduce the statistical properties of spectrum occupancy patterns in real systems. The chapter begins with the description of various time-dimension modeling approaches (in discrete and continuous time) along with models for time-correlation properties. Subsequently, joint time-frequency models as well as space-dimension models are explained in detail. Finally, the chapter concludes with a discussion on the combination and integration of the presented models into a unified modeling approach where the time, frequency and space dimensions of spectrum usage can be modeled simultaneously.


Probability Density Function Duty Cycle Busy Period Generalise Pareto Distribution Idle Period 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Spaulding D, Hagn GH (1977) On the definition and estimation of spectrum occupancy. IEEE Trans Electromagn Compat EMC-19(3):269–280CrossRefGoogle Scholar
  2. 2.
    Laycock PJ, Morrell M, Gott GF, Ray AR (1988) A model for HF spectral occupancy. In: Proceedings of the fourth international conference on HF radio systems and techniques, pp 165–171Google Scholar
  3. 3.
    López-Benítez M, Casadevall F (2011) An overview of spectrum occupancy models for cognitive radio networks. In: Proceedings of the IFIP international workshop on performance evaluation of cognitive radio networks (PE-CRN 2011), 13 May 2011, pp 1–10Google Scholar
  4. 4.
    Ibe OC (2009) Markov processes for stochastic modelling. Academic Press, BurlingtonGoogle Scholar
  5. 5.
    López-Benítez M, Casadevall F (2011) Empirical time-dimension model of spectrum use based on discrete-time Markov chain with deterministic and stochastic duty cycle models. IEEE Trans Veh Technol 60(6):2519–2533CrossRefGoogle Scholar
  6. 6.
    Wang Z, Salous S (2011) Spectrum occupancy statistics and time series models for cognitive radio. J Signal Process Syst 62(2):145–155CrossRefGoogle Scholar
  7. 7.
    Chen D, Yin S, Zhang Q, Liu M, Li S (2009) Mining spectrum usage data: a large scale spectrum measurement study. In: Proceedings of the 15th ACM annual international conference on mobile computing and networking (MobiCom 2009), Sept 2009, pp 13–24Google Scholar
  8. 8.
    Blaschke V, Jaekel H, Renk T, Kloeck C, Jondral FK (2007) Occupation measurements supporting dynamic spectrum allocation for cognitive radio design. In: Proceedings of the 2nd international conference on cognitive radio oriented wireless networks and communications (CrownCom 2007), Aug 2007, pp 50–57Google Scholar
  9. 9.
    Papoulis A, Pillai SU (2002) Probability, random variables, and stochastic processes, 4th edn. McGraw-Hill, BostonGoogle Scholar
  10. 10.
    Kumaraswamy P (1980) A generalized probability density function for double-bounded random processes. J Hydrol 46(1–2):79–88CrossRefGoogle Scholar
  11. 11.
    Jones MC (2009) Kumaraswamy’s distribution: a beta-type distribution with some tractability advantages. Stat Methodol 6(1):70–81MathSciNetzbMATHCrossRefGoogle Scholar
  12. 12.
    Geirhofer S, Tong L, Sadler BM (2006) A measurement-based model for dynamic spectrum access in WLAN channels. In: Proceedings of the IEEE military communications conference (MILCOM 2006), Oct 2006, pp 1–7Google Scholar
  13. 13.
    Geirhofer S, Tong L, Sadler BM (2006) Dynamic spectrum access in WLAN channels: empirical model and its stochastic analysis. In: Proceedings of the first international workshop on technology and policy for accessing spectrum (TAPAS 2006), Aug 2006, pp 1–10Google Scholar
  14. 14.
    Geirhofer S, Tong L, Sadler BM (2007) Dynamic spectrum access in the time domain: modeling and exploiting white space. IEEE Commun Mag 45(5):66–72CrossRefGoogle Scholar
  15. 15.
    Stabellini L (2010) Quantifying and modeling spectrum opportunities in a real wireless environment. In: Proceedings of the IEEE wireless communications and networking conference (WCNC 2010), April 2010, pp 1–6Google Scholar
  16. 16.
    Wellens M, Riihijärvi J, Mähönen P (2009) Empirical time and frequency domain models of spectrum use. Phys Commun 2(1–2):10–32Google Scholar
  17. 17.
    Öztekin T (2005) Comparison of parameter estimation methods for the three-parameter generalized Pareto distribution. Turk J Agric Forest 29(6):419–428Google Scholar
  18. 18.
    López-Benítez M, Casadevall F (2011) Modeling and simulation of time-correlation properties of spectrum use in cognitive radio. In: Proceedings of the 6th international ICST conference on cognitive radio oriented wireless networks and communications (CrownCom 2011), June 2011, pp 1–5Google Scholar
  19. 19.
    Devroye L (1986) Non-uniform random variate generation. Springer, New YorkGoogle Scholar
  20. 20.
    Tavares GN, Petrolino A (2011) On the generation of correlated Gaussian random variates by inverse DFT. IEEE Trans Commun 59(1):45–51CrossRefGoogle Scholar
  21. 21.
    Hotelling H, Pabst MR (1936) Rank correlation and tests of significance involving no assumption of normality. Ann of Math Stat 7(1):29–43zbMATHCrossRefGoogle Scholar
  22. 22.
    López-Benítez M, Casavadell F, López-Pérez D, Vasilakos AV (2011) Modeling and simulation of joint time-frequency properties of spectrum usage in cognitive radio. In: Proceedings of the 4th international conference on cognitive radio and advanced spectrum management (CogART 2011), Invited paper, Barcelona, Spain, Oct 2011, pp 1–5Google Scholar
  23. 23.
    López-Benítez M, Casadevall F (2010) Spatial duty cycle model for cognitive radio. In: Proceedings of the 21st Annual IEEE international symposium on personal, indoor and mobile radio communications (PIMRC 2010), Sept 2010, pp 1631–1636Google Scholar
  24. 24.
    Yücek T, Arslan H (2009) A survey of spectrum sensing algorithms for cognitive radio applications. IEEE Commun Surv Tutor 11(1):116–130 First QuarterCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2012

Authors and Affiliations

  1. 1.Universitat Politècnica de Catalunya (UPC)BarcelonaSpain

Personalised recommendations