Skip to main content
SpringerLink
Log in

A Method to Enhance the Throughput of Cognitive Radio Network Using Kullback Leibler Divergence with Optimized Sensing Time (KLDOST)

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Efficient, quick and reliable spectrum sensing in consonance with maximum throughput is required at the cognitive radios to achieve maximum utilization of the network. In cognitive radio environment, the intelligent secondary users shall effectively sense the presence of primary user signal to avoid interference, in order to achieve maximum operational feasibility. A cooperative sensing scheme using Kullback Leibler divergence with higher throughput and lower sensing time to avail and identify the underutilized frequency spectrum is presented in this paper. The optimized sensing scheme utilizes the log-likelihood ratio estimated at the secondary user during each sensing instance, with fusion centre acquiring and assimilating this information to estimate the likelihood of spectrum availability. The optimized scheme is implemented in a robust manner assuming the signal and noise are gaussian distributed. Simulation results highlighting the competitive edge of the proposed scheme, with higher throughput over various false alarm probability and signal to noise ratio are presented.

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
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. SpiderCloud Wireless. (2013). Poor in-building coverage leaves valuable licensed spectrum under-utilized. Press release.

  2. Quan, Z., Cui, S., & Sayed, A. H. (2008). Optimal linear cooperation for spectrum sensing in cognitive radio networks. IEEE Journal of Selected Topics in Signal Processing, 2(1), 28–40.

    Article  Google Scholar 

  3. Akyildiz, I. F., Lee, W., Vuran, M. C., & Mohanty, S. (2006). Next generation/dynamic spectrum access/cognitive radio wireless networks: A survey. Computer Networks, 50, 2127–2159.

    Article  Google Scholar 

  4. Nekovee, M. (2010). A survey of cognitive radio access to TV white spaces. International Journal of Digital Multimedia Broadcasting, 1–11 (Article ID 236568).

  5. Haykin, S. (2005). Cognitive radio: Brain-empowered wireless communications. IEEE Journal on Selected Areas in Communications, 23(2), 201–220.

    Article  Google Scholar 

  6. Mitola, J, I. I. I., & Maguire, G. Q. (1999). Cognitive radio: Making software radios more personal. IEEE Personal Communications, 6(4), 13–18.

    Article  Google Scholar 

  7. Matsui, M., Shiba, H., Akabane, K., & Uehara, K. (2007). A novel cooperative sensing technique for cognitive radio. In Proceedings of the IEEE 18th international symposium on personal, indoor, mobile radio communications (PIMRC), (pp 1–5).

  8. Furtado, A., Irio, L., Oliveira, R., Bernardo, L., & Dinis, R. (2015). Spectrum sensing performance in cognitive radio networks with multiple primary users. IEEE Transactions on Vehicular Technology, 65, 1564–1574.

    Article  Google Scholar 

  9. Liang, Y. C., Zeng, Y., Peh, E. C. Y., & Hoang, A. T. (2008). Sensing-throughput tradeoff for cognitive radio networks. IEEE Transactions on Wireless Communications, 7(4), 1326–1337.

    Article  Google Scholar 

  10. Unnikrishnan, J., & Veeravalli, V. V. (2008). Cooperative sensing for primary detection in cognitive radio. IEEE Journal of Selected Topics in Signal Processing, 2(1), 18–27.

    Article  Google Scholar 

  11. Wald, A. (1947). Sequential analysis. New York: Wiley.

    MATH  Google Scholar 

  12. Wald, A. (1944). On cumulative sums of random variables. The Annals of Mathematical Statistics, 15(3), 283–296.

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of ECE, Thanthai Periyar Government Institute of Technology, Vellore, India

    Munisamy Manimegalai

  2. Department of ECE, Anna University, Chennai, India

    M. A. Bhagyaveni

Authors
  1. Munisamy Manimegalai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. A. Bhagyaveni
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Munisamy Manimegalai.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Manimegalai, M., Bhagyaveni, M.A. A Method to Enhance the Throughput of Cognitive Radio Network Using Kullback Leibler Divergence with Optimized Sensing Time (KLDOST). Wireless Pers Commun 109, 1645–1660 (2019). https://doi.org/10.1007/s11277-019-06643-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-019-06643-0

Keywords

Search

Navigation