Throughput Analysis of Multichannel Cognitive Radio Networks Based on Stochastic Geometry

  • Seunghee Lee
  • Ganguk HwangEmail author
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 383)


In this paper, we consider an underlay type cognitive radio network with multiple secondary users who contend to access multiple heterogeneous primary channels. With the help of stochastic geometry we develop a new analytical model to analyze the throughput of a random channel access protocol where each secondary user determines whether to access a primary channel based on a given access probability. Due to the interference-free region that we newly introduce we can easily analyze the throughput of a random channel access protocol. Numerical examples are provided to validate our analysis.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Goldsmith, A., Jafar, S.A., Maric, I., Srinivasa, S.: Breaking spectrum gridlock with cognitive radios: an information theoretic perspective. Proc. IEEE 97(5), 894–914 (2009)CrossRefGoogle Scholar
  2. 2.
    Stoyan, D., Kendall, W., Mecke, J.: Stochastic Geometry and Its Applications, 2nd edn. John Wiley and Sons (1996)Google Scholar
  3. 3.
    Haenggi, M., Andrews, J.G., Baccelli, F., Dousse, O., Franceschetti, M.: Stochastic Geometry and Random Graphs for the Analysis and Design of Wireless Networks. IEEE J. Select. Areas Commun. 27(7), September 2009CrossRefGoogle Scholar
  4. 4.
    Yin, C., Chen, C., Liu, T., Cui, S.: Generalized results of transmission capacities for overlaid wireless networks. In: Proc. IEEE Int. Symp. Inf. Theory, Seoul, Korea, pp. 1774–1778, June 2009Google Scholar
  5. 5.
    Vaze, R.: Transmission capacity of spectrum sharing ad hoc networks with multiple antennas. IEEE Trans. Wireless Commun. 10(7), 2334–2340 (2011)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Lee, J., Andrews, J.G., Hong, D.: Spectrum-sharing transmission capacity. IEEE Trans. Wireless Commun. 10(9), 3053–3063 (2011)CrossRefGoogle Scholar
  7. 7.
    Nguyen, T.V., Baccelli, F.: A probabilistic model of carrier sensing based cognitive radio. In: 2010 IEEE Symposium on New Frontiers in Dynamic Spectrum, pp. 1–12, April 2010Google Scholar
  8. 8.
    Lee, C., Haenggi, M.: Interference and Outage in Poisson Cognitive Networks. IEEE Trans. Wireless Commun. 11(4), 1392–1401 (2012)CrossRefGoogle Scholar
  9. 9.
    Song, X., Yin, C., Liu, D., Zhang, R.: Spatial opportunity in cognitive radio networks with threshold-based opportunistic spectrum access. In: 2013 IEEE International Conference on Communications (ICC), pp. 2695–2700, June 2013Google Scholar
  10. 10.
    Busson, A., Jabbari, B., Babaei, A., Vèque, V.: Interference and Throughput in Spectrum Sensing Cognitive Radio Networks using Point Processes. Journal of Communications and Networks 16(1), 67–80 (2014)CrossRefGoogle Scholar
  11. 11.
    Moltchanov, D.: Distance distributions in random networks. Ad Hoc Networks 10(6), 1146–1166 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Mathematical SciencesKorea Advanced Institute of Science and TechnologyDaejeonRepublic of Korea

Personalised recommendations