Skip to main content
SpringerLink
Log in

Dominant Interferers in Cognitive Radio Network

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Aggregate interference from a large number of cognitive radios (CRs) is a deterring factor to the implementation of spectrum sharing strategies. Even if all CRs implement spectrum sensing and are made to transmit at the same power, we show that there are CRs which are the major contributors of the aggregate interference. We call them the dominant interferers. Any attempt of alleviating the interference should first target the region where the dominant interferers are located. In this paper, we explain the method of determining their respective distances from the primary system, based on a theoretical model of their spatial distribution. This is accompanied by some numerical examples that demonstrate the use of the method.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Menon, R., Buehrer, R. M., & Reed, J. H. (2008). On the impact of dynamic spectrum sharing techniques on legacy radio systems. IEEE Transactions on Wireless Communications, 7(11), 4198–4207.

    Article  Google Scholar 

  2. Rabbachin, A., Quek, T. Q. S., Shin, H., & Win, M. Z. (2011). Cognitive network interference. IEEE Journal on Selected Areas in Communications, 29(2), 480–493.

    Article  Google Scholar 

  3. Chen, Z., Wang, C.-X., Hong, X., Thompson, J., Vorobyov, S. A., Ge, X., et al. (2012). Aggregate interference modeling for cognitive radio networks with power and contention control. IEEE Transactions on Communications, 60(2), 456–467.

    Article  Google Scholar 

  4. Hanif, M. F., & Smith, P. J. (2010). Level crossing rates of interference in cognitive radio networks. IEEE Transactions on Wireless Communications, 9(4), 1283–1287.

    Article  Google Scholar 

  5. Ghasemi, A., & Sousa, E. S. (2008). Interference aggregation in spectrum-sensing cognitive wireless networks. IEEE Journal of Selected Topics in Signal Processing, 2(1), 41–56.

    Article  Google Scholar 

  6. Mahmood, N. H., Yilmaz, F., Alouini, M.-S., & Øien, G. E. (2014). Heterogeneous next-generation wireless network interference model—And its applications. Transactions on Emerging Telecommunications Technologies, 25(5), 563–575.

    Google Scholar 

  7. Urkowitz, H. (1967). Energy detection of unknown deterministic signals. Proceedings of the IEEE, 55(4), 523–531.

    Article  Google Scholar 

  8. Digham, F. F., Alouini, M.-S., & Simon, M. K. (2003). On the energy detection of unknown signals over fading channels. In Proceedings of IEEE ICC (pp. 3575–3579).

  9. Ghasemi, A., & Sousa, E. S. (2007). Opportunistic spectrum access in fading channels through collaborative sensing. Journal of Communications, 2(2), 71–82.

    Article  Google Scholar 

  10. Foo, Y.-L. (2013). Performance of cooperative spectrum sensing under Rician and Nakagami fading. Wireless Personal Communications, 70(4), 1541–1551.

    Article  Google Scholar 

  11. Foo, Y.-L. (2013). Deployment of wireless regional area network and its impact on DTV service coverage. Annals of Telecommunications, 68(11–12), 691–703.

    Article  Google Scholar 

  12. Foo, Y.-L. (2013). Cooperative sensing in wireless regional area network to improve DTV coverage prediction. Wireless Personal Communications, 72(4), 2339–2360.

    Article  Google Scholar 

  13. Nuttall, A. H. (1975). Some integrals involving the Q M function. IEEE Transactions on Information Theory, 21(1), 95–96.

    Article  MathSciNet  MATH  Google Scholar 

  14. Nuttall, A. H. (1974). Some integrals involving the Q M function. Naval Underwater Systems Center (NUSC) technical report, TR 4755, 1974.

  15. Forsythe, G. E., Malcolm, M. A., & Moler, C. B. (1976). Computer methods for mathematical computations. Englewood Cliffs: Prentice-Hall.

    MATH  Google Scholar 

  16. Vu, M., Devroye, N., & Tarokh, V. (2009). On the primary exclusive region of cognitive networks. IEEE Transactions on Wireless Communications, 8(7), 3380–3385.

    Article  Google Scholar 

  17. Gander, W., & Gautschi, W. (2000). Adaptive quadrature—Revisited. BIT, 40, 84–101.

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Engineering, Multimedia University, 63100, Cyberjaya, Malaysia

    Yee-Loo Foo

Authors
  1. Yee-Loo Foo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yee-Loo Foo.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Foo, YL. Dominant Interferers in Cognitive Radio Network. Wireless Pers Commun 91, 1271–1284 (2016). https://doi.org/10.1007/s11277-016-3527-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-016-3527-y

Keywords

Search

Navigation