Skip to main content
SpringerLink
Log in

Distributed Spectrum Sensing Using Radio Environment Maps in Cognitive Radio Networks

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Ever increasing development of wireless devices and wireless networks have increased the value of spectral space. Many efforts have been conducted to increase spectral utilization. In this paper, a novel distributed spectrum sensing method is presented. This method efficiently increases the spectral throughput of the network. In this algorithm, distributed Kalman filter, which is modified to increase estimation accuracy, is used to estimate position, velocity, and power of primary transmitters. These data are used to select spectrum holes optimally and increase spectral utilization compared to centralized methods. Obtained results are evaluated through practical implementations and simulations. Innovations of this research include introducing and employing a linear model for estimating the position of a transmitter using received power in the line of sight and non-line of sight conditions, modifying extended Kalman filter and implementation of distributed spectrum sensing; advantages of this method are illustrated compared to other spectrum sensing methods.

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

Similar content being viewed by others

References

  1. Masonta, M. T., Mzyece, M., & Ntlatlapa, N. (2013). Spectrum decision in cognitive radio networks: A survey. IEEE Communications Surveys and Tutorials, 15(5), 1088–1107.

    Article  Google Scholar 

  2. Xiao, L., Chen, T., Liu, J., & Dai, H. (2015). Anti-jamming transmission stackelberg game with observation errors. IEEE Communications Letters, 19(6), 949–952.

    Article  Google Scholar 

  3. Lu, L., Zhou, X., Onunkwo, U., & Li, G. Y. (2012). Ten years of research in spectrum sensing and sharing in cognitive radio. Eurasip Journal on Wireless Communications and Networking, 2012(28), 1–16.

    Google Scholar 

  4. Liang, Y. C., Chen, K. C., Li, G. Y., & Mahonen, P. (2013). Cognitive radio networking and communications: An overview. IEEE Wireless Communications, 13(1), 74–81.

    Google Scholar 

  5. Sun, H., Nallanathan, A., Wang, C. X., & Chen, Y. (2015). Wideband spectrum sensing for cognitive radio networks: A survey. IEEE Communications Letters, 60(7), 3386–3407.

    Google Scholar 

  6. Bouraoui, R., & Besbes, H. (2016). Cooperative spectrum sensing for cognitive radio networks: Fusion rules performance analysis. In 2016 International wireless communications and mobile computing conference (IWCMC).

  7. Bazerque, J. A., & Giannakis, G. B. (2010). Distributed spectrum sensing for cognitive radio networks by exploiting sparsity. IEEE Transactions on Signal Processing, 58(3), 1847–1862.

    Article  MathSciNet  MATH  Google Scholar 

  8. Grönroos, S., Nybom, K., Björkqvist, J., Hallio, J., Auranen, J., & Ekman, R. (2016). Distributed spectrum sensing using low cost hardware. Journal of Signal Processing Systems, 83(1), 5–17.

    Article  Google Scholar 

  9. Yilmaz, H. B., & Tugcu, T. (2015). Location estimation-based radio environment map construction in fading channels. Wireless Communications and Mobile Computing, 15(3), 561–570.

    Article  Google Scholar 

  10. Pesko, M., Javornik, T., Košir, A., Štular, M., & Mohorčič, M. (2014). Radio environment maps: The survey of construction methods. KSII Transactions on Internet and Information Systems, 8(11), 3789–3809.

    Google Scholar 

  11. Denkovski, D., Atanasovski, V., Gavrilovska, L., Riihijärvi, J., & Mähönen, P. (2012). Reliability of a radio environment map: Case of spatial interpolation techniques. In 7th international ICST conference on cognitive radio oriented wireless networks and communications (CROWNCOM).

  12. Ulaganathan, S., Deschrijver, D., Pakparvar, M., Couckuyt, I., Liu, W., Plets, D., et al. (2016). Building accurate radio environment maps from multi-fidelity spectrum sensing data. Wireless Networks, 22(8), 2551–2562.

    Article  Google Scholar 

  13. Bazerque, J. A., & Giannakis, G. B. (2015). Distributed spectrum sensing for cognitive radio networks by exploiting sparsity. IEEE Transactions on Signal Processing, 58(3), 1847–1863.

    Article  MathSciNet  MATH  Google Scholar 

  14. Pesko, M., Javornik, T., Vidmar, L., Košir, A., Štular, M., & Mohorčič, M. (2015). The indirect self-tuning method for constructing radio environment map using omnidirectional or directional transmitter antenna. Eurasip Journal on Wireless Communications and Networking, 2015(1), 1–12.

    Article  Google Scholar 

  15. Shi, X., Mao, G., Yang, Z., & Chen, J. (2016). Localization algorithm design and performance analysis in probabilistic los/nlos environment. In IEEE wireless communications symposium (ICC 2016).

  16. Zarchan, P., & Musoff, H. (2000). Fundamentals of Kalman filtering: A practical approach. American Institute of Aeronautics and Astronautics incorporated.

  17. Li, L., Chambers, J. A., Lopes, C. G., & Sayed, A. H. (2010). Distributed estimation over an adaptive incremental network based on the affine projection algorithm. IEEE Transactions on Signal Processing, 58(1), 151–163.

    Article  MathSciNet  MATH  Google Scholar 

  18. Tu, S.-Y., & Sayed, A. H. (2012). Diffusion strategies outperform consensus strategies for distributed estimation over adaptive networks. IEEE Transactions on Signal Processing, 60(12), 6217–6234.

    Article  MathSciNet  MATH  Google Scholar 

  19. Cattivelli, F. S., & Sayed, A. H. (2010). Diffusion LMS strategies for distributed estimation. IEEE Transactions on Signal Processing, 58(3), 1035–1048.

    Article  MathSciNet  MATH  Google Scholar 

  20. Ezzati, N., Taheri, H., & Tucu, T. (2016). Optimised sensor network for transmitter localisation and radio environment mapping. IET Communications, 10(16), 2170–2178.

    Article  Google Scholar 

  21. Tu, S.-Y., & Sayed, A. H. (2011). Diffusion strategies outperform consensus strategies for distributed estimation over adaptive networks unknown primary signal arrival time. IEEE Transactions on Communications, 59(7), 1779–1785.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Electrical Engineering Department, Amirkabir University of Technology, Tehran, Iran

    Nematollah Ezzati & Hassan Taheri

Authors
  1. Nematollah Ezzati
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hassan Taheri
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nematollah Ezzati.

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

Ezzati, N., Taheri, H. Distributed Spectrum Sensing Using Radio Environment Maps in Cognitive Radio Networks. Wireless Pers Commun 101, 2241–2254 (2018). https://doi.org/10.1007/s11277-018-5814-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-018-5814-2

Keywords

Search

Navigation