Log in

A Novel Mathematical Model for Energy Detection Based Spectrum Sensing in Cognitive Radio Networks

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Spectrum sensing is the quintessence of cognitive radio network and is influenced by uncertain noise at low SNR. In such a scenario sensing duration imposes a constraint on the sensing performance. This paper presents a novel mathematical approach to obtain optimal sensing duration (number of samples) in presence of noise uncertainty for energy detection method. The effect of noise uncertainty on number of sensed samples has been analyzed and a novel approach has been presented to correlate the sensing duration with SNR to attain desired performance in terms of PFA (Probability of False Alarm) and PD (Probability of Detection).

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

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Germany)

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Valenta, V., Maršálek, R., Baudoin, G., et al. (2010). Survey on spectrum utilization in Europe: Measurements, analyses and observations. In CROWNCOM (pp. 1–6). Cannes, France.

  2. Federal Communications Commission. (2004). Notice of proposed rule making, FCC 04-113: Unlicensed operation in the TV broadcast bands (ET Docket No. 04-186). Retrieved May 25, 2004, from http://www.fcc.gov/sptf/headlines2004.html.

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  5. Letaief, K. B., & Zhang, W. (2009). Cooperative communications for cognitive radio. Proceedings of the IEEE,97(5), 878–893.

    Article  Google Scholar 

  6. Vadivelu, K. S. R., & Vijayakumari, V. (2014). Matched filter based spectrum sensing for cognitive radio at low signal to noise ratio. Journal of Theoretical & Applied Information Technology, 62(1), 107–113.

    Google Scholar 

  7. Hosseini, S. M. A. T., Amindavar, H., & Ritcey, J. A. (2010). A new cyclo-stationary spectrum sensing approach in cognitive radio. In Proceedings of IEEE SPAWC’10, Marrakech (pp. 1–4).

  8. Tian, Z., & Giannakis, G. B. (2006). A wavelet approach to wideband spectrum sensing for cognitive radios. In Proceedings of CROWNCOM’06, Mykonos Island, Greece.

  9. Zeng, Y., & Liang, Y. C. (2007) Covariance based signal detections for cognitive radio. In Proceedings of IEEE international symposium on new frontiers in dynamic spectrum access networks, Dublin, Ireland (pp. 202–207).

  10. Yucek, T., & Arslan, H. (2009). A survey of spectrum sensing algorithm for cognitive radio applications. IEEE Communication Survey & Tutorials,11(1), 116–130.

    Article  Google Scholar 

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

    Article  Google Scholar 

  12. 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’03 (pp. 3575–3579).

  13. Yu, G., Long, C., & **ang, M. (2012). A novel spectrum detection scheme based on dynamic threshold in cognitive radio systems. Research Journal of Applied Sciences, Engineering and Technology,4(21), 4245–4251.

    Google Scholar 

  14. Chabbra, K., Mahendru, G., Banerjee, P. (2014). Effect of dynamic threshold and noise uncertainty in energy detection spectrum sensing technique for cognitive radio systems. In Proceedings of the IEEE international conference on signal processing and integrated networks (SPIN), India (pp. 361–377).

  15. Tandra, R., & Sahai, A. (2008). SNR walls for signal detection. IEEE Journal of Selected Topics in Signal Processing,2(1), 4–17.

    Article  Google Scholar 

  16. Yin, W., Ren, P., Cai, J., & Su, Z. (2013). Performance of energy detector in the presence of noise uncertainty in cognitive radio networks. Wireless Networks,19(5), 629–638.

    Article  Google Scholar 

  17. Shellhammer, S. J. (2008). Spectrum sensing in IEEE 802.22. In 1st IAPR workshop on cognitive information processing.

  18. Cacciapuoti, A., Caleffi, M., Marino, F., & Paura, L. (2014). Sensing-time optimization in cognitive radio enabling smart grid. In Proceedings of EMTC (pp. 1–6).

  19. Arar, A., Masri, A., & Ghannam, H. (2017). A proposed scheme for dynamic threshold versus noise uncertainty in cognitive radio networks (DTNU). Wireless Personal Communication,96, 4543–4555.

    Article  Google Scholar 

  20. Avila, J., & Thenmozhi, K. (2015). Enrichment of adaptive threshold in cognitive radio. Asian Journal of Scientific Research,8, 333–341.

    Article  Google Scholar 

  21. Zhu, J., Xu, Z., Wang, F., Huang, B., & Zhang, B. (2008) Double threshold energy detection of cooperative spectrum sensing in cognitive radio. In Proceedings on international conference on cognitive radio oriented wireless networks and communications CrownCom‘08 (pp. 1–5).

  22. **e, J., & Chen, J. (2012). An adaptive double-threshold spectrum sensing algorithm under noise uncertainty. In 2012 IEEE 12th international conference on computer and information technology, Chengdu (pp. 824–827).

  23. Verma, P., & Singh, B. (2016). On the decision fusion for cooperative spectrum sensing in cognitive radio networks. Wireless Networks, 23(7), 1–10.

    Google Scholar 

  24. Atapattu, S., Tellambura, C., & Jiang, H. (2011). Spectrum sensing via energy detector in low SNR. In Proceedings of IEEE ICC (pp. 1–5).

  25. Kay, S. M. (1993). Fundamentals of statistical signal processing: Estimation theory. Upper Saddle River, NJ: Prentice-Hall Inc.

    MATH  Google Scholar 

  26. He, Y., Martin, R., & Bilgic, A. M. (2010). Approximate iterative least squares algorithms for GPS positioning. In Proceedings of the IEEE international symposium on signal processing and information technology (ISSPIT ‘10), Luxor, Egypt (pp. 231–236).

  27. Söderström, T., & Stewart, G. W. (1974). On the numerical properties of an iterative method for computing the Moore–Penrose generalized inverse. SIAM Journal on Numerical Analysis,11(1), 61–74.

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Garima Mahendru.

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

Mahendru, G., Shukla, A. & Banerjee, P. A Novel Mathematical Model for Energy Detection Based Spectrum Sensing in Cognitive Radio Networks. Wireless Pers Commun 110, 1237–1249 (2020). https://doi.org/10.1007/s11277-019-06783-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-019-06783-3

Keywords

Navigation