Wireless Personal Communications

, Volume 92, Issue 4, pp 1675–1694 | Cite as

A Lightweight Spectrum Occupancy and Service Time Model for Centralized Cognitive Radio Networks

Article

Abstract

In cognitive radio networks (CRNs), secondary users (SUs) exploit the unused or sparsely used spectrum by primary users (PUs) without causing any harmful interference. Consequently, spectrum occupancy modeling appears as an essential task for CRN operations. In this paper, spectrum occupancy has been modeled using a queueing theory based approach in order to analyze the performance of CRNs in terms of network capacity, number of cognitive radio users waiting for service, and average waiting time. A compact model is presented for a CRN, where the queue adopted has variable service capacity and can be considered as a multi-service queue with server failure where each channel acts as a server. When a channel is occupied by a PU, it is regarded as a server failure for SUs. Using the probability generating function, the closed-form expressions of various performance parameters for different arrival distributions are derived. Numerical results for the remaining services for SUs, the expected number of SUs, and average waiting time as the CRN capacity, average service demands and average arrival rate vary, are illustrated.

Keywords

Queueing theory Spectrum occupancy modeling Variable network capacity Probability generating function 

References

  1. 1.
    Zaman, B., Abbas, Z.H., & Li, F.Y. (2015). Spectrum occupancy and residual service analysis in CRNs using a multi-server queueing model. In Proceedings of the IEEE vehicular technology conference (VTC), Glasgow, UK, May 2015.Google Scholar
  2. 2.
    Das, D., & Das, S. (2015). A survey on spectrum occupancy measurement for cognitive radio. Wireless Personal Communications, 85(4), 1–18.CrossRefGoogle Scholar
  3. 3.
    Akyildiz, I. F., Lee, W. Y., Vuran, M. C., & Mohanty, S. (2006). NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey. Computer Networks, 50(13), 2127–2159.CrossRefMATHGoogle Scholar
  4. 4.
    Mitola, J., & Maguire, J. G. Q. (1999). Cognitive radio: Making software radios more personal. IEEE Personal Communications, 6(4), 13–18.CrossRefGoogle Scholar
  5. 5.
    Ghosh, C., Pagadarai, S., Agrawal, D. P., & Wyglinski, A. M. (2010). A framework for statistical wireless spectrum occupancy modeling. IEEE Transactions on Wireless Communications, 9(1), 38–44.CrossRefGoogle Scholar
  6. 6.
    Lopez-Benitez, M., & Casadevall, F. (2014). A framework for multidimensional modelling of spectrum occupancy in the simulation of cognitive radio systems. In Proceedings of the international symposium on communication systems, networks and digital signal processing (CSNDSP), Manchester, UK, July 2014.Google Scholar
  7. 7.
    Ghosh, C., Cordeiro, C., Agrawal, D. P., & Rao, M. B. (2009). MarkovChain existence and hidden markov models in spectrum sensing. In IEEE international conference on pervasive computing and communications, Galveston, TX, USA, March 2009.Google Scholar
  8. 8.
    Lopez-Benitez, M., & Casadevall, F. (2011). Discrete-time spectrum occupancy model based on Markov chain and duty cycle models. In Proceedings of the IEEE symposium on new frontiers in dynamic spectrum access networks, Aachen, Germany, May 2011.Google Scholar
  9. 9.
    Zhang, J., Zhu, H., & Zhi, H. (2010). On channels activity of opportunistic spectrum sharing with homogeneous primary users. In Proceedings of the international conference on wireless communications networking and mobile computing (WiCOM), Chengdu, China, September 2010.Google Scholar
  10. 10.
    Wijedasa, S., & Alfa, A. S. (2012). An improved channel model for cognitive radio. In Proceedings of the IEEE international conference on communications (ICC), Ottawa, Canada, June 2012.Google Scholar
  11. 11.
    Zahmati, A. S., Fernando, X., & Grami, A. (2010). Steady-state markov chain analysis for heterogeneous cognitive radio networks. In Proceedings of the IEEE sarnoff symposium, Princeton, NJ, USA, April 2010.Google Scholar
  12. 12.
    Ghosh, C., Pagadarai, S., Agrawal, D. P., & Wyglinski, A. M. (2009). Queueing theory representation and modeling of spectrum occupancy employing radio frequency measurements. In Proceedings of the IEEE vehicular technology conference (VTC), Anchorage, AL, USA, September 2009.Google Scholar
  13. 13.
    Li, H., & Han, Z. (2010). Queuing analysis of dynamic spectrum access subject to interruptions from primary users. In Proceedings of the international conference on cognitive radio oriented wireless networks and communications (CROWNCOM), Cannes, France, June 2010.Google Scholar
  14. 14.
    Chang, W. S., & Jang, W. M. (2014). Spectrum occupancy of cognitive radio networks: A queueing analysis using retrial queue. IET Networks, 3(3), 218–227.CrossRefGoogle Scholar
  15. 15.
    Bruneel, H., & Wuyts, I. (1994). Analysis of discrete-time multiserver queueing models with constant service times. Operations Research Letters, 15(5), 231–236.CrossRefMATHGoogle Scholar
  16. 16.
    Chakwarthy, S. R. (2009). Analysis of a multi-server queue with markovian arrivals and synchronous phase type vacations. Asia-Pacific Journal of Operational Research, 26(01), 85–113.MathSciNetCrossRefGoogle Scholar
  17. 17.
    Gao, P., Wittevrongel, S., & Bruneel, H. (2004). Discrete-time multiserver queues with geometric service times. Computers and Operations Research, 31(1), 81–99.CrossRefMATHGoogle Scholar
  18. 18.
    Bruneel, H., Walraevens, J., Claeys, D., & Wittevrongel, S. (2012). Analysis of a discrete-time queue with geometrically distributed service capacities. In Proceedings of the international conference on analytical and stochastic modeling techniques and applications, Grenoble, France, June 2012.Google Scholar
  19. 19.
    Kobayashi, H., Mark, B. L., & Turin, W. (2011). Probability,random processes, and statistical analysis: Applications tocommunications, signal processing, queueing theory and mathematicalfinance. Cambridge: cambridge University Press.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Faculty of Electronic EngineeringGhulam Ishaq Khan Institute of Engineering Sciences and TechnologyTopi, SwabiPakistan
  2. 2.Department of Information and Communication TechnologyUniversity of Agder (UiA)GrimstadNorway

Personalised recommendations