Nothing Special   »   [go: up one dir, main page]
Skip to main content

Advertisement

Springer Nature Link
Log in

On Energy-Based Quality of Detection (QoD) for Cognitive Radio Sensor Networks

  • Published:
International Journal of Wireless Information Networks Aims and scope Submit manuscript

Abstract

An energy-based primary signal detection framework is proposed to study the quality of detection (QoD) performance for cognitive radio sensor networks (CRSNs) under single cognitive sensor (CS) detection and multiple CS detection. We use hypothesis testing to first derive the exact solution and then the approximate solution (with less computational complexity) of the QoD metrics. Based on the approximate solution, we develop an adaptive QoD control scheme to maintain the QoD requirements. Numerical simulations are provided to validate the analysis. The proposed framework and analytic results are expected to be applied to different aspects (e.g., protocol design, network deployment) of CRSNs.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /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 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. S. Tang, Y. Zhang, L. Zhang, and R. Yu, “Spectrum-Efficient Wireless Sensor Networks,” Hindawi International Journal of Distributed Sensor Networks, Vol. 2015, Article ID 614680, 2015. (http://dx.doi.org/10.1155/2015/614680)

  2. I. Howitt and J. A. Gutierrez, “IEEE 802.15.4 Low Rate - Wireless Personal Area Network Coexistence Issues,” in Proc. IEEE WCNC ‘03, pp. 1481-1486, March 2003.

  3. S. Y. Shin, H. S. Park, S. Choi and W. H. Kwon, Packet error rate analysis of zigbee under WLAN and Bluetooth interferences, IEEE Trans. on Wireless Communications, Vol. 6, pp. 2825–2830, 2007.

    Article  Google Scholar 

  4. J Mitola, Cognitive radio: an integrated agent architecture for software defined Radio, Ph.D. dissertation, Royal Institute of Technology, Stockholm, Sweden, 2000.

  5. H. L. Van Trees, Detection, Estimation and Modulation Theory: Part I, Wiley-Interscience, 2001.

  6. H. Urkowitz, Energy detection of unknown deterministic signals, Proceedings of IEEE, Vol. 55, pp. 523–531, 1967.

    Article  Google Scholar 

  7. F. Digham, M.-S. Alouini and M. K. Simon, On the energy detection of unknown signals over fading channels, IEEE Trans. on Communications, Vol. 55, No. 1, pp. 21–24, 2007.

    Article  Google Scholar 

  8. H. Sun, D. I. Laurenson, and C.-X. Wang, “Computationally Tractable Model of Energy Detection Performance over Slow Fading Channels,” IEEE Communications Letters, vol. 14, No. 10, pp. 924-926, Oct. 2010

  9. S. Atapattu, C. Tellambura and H. Jiang, “Energy Detection Based Cooperative Spectrum Sensing in Cognitive Radio Networks,” IEEE Trans. on Wireless Communications, Vol. 10, No. 4, pp. 1232-1241, Apr. 2011.

  10. M. Lopez-Benitez and F. Casadevall, “Signal Uncertainty in Spectrum Sensing for Cognitive Radio,” IEEE Trans. on Communications, Vol. 61, No. 4, pp. 1231-1241, April 2013.

  11. A. Bagwari and G. S. Tomar, Adaptive double-threshold based energy detector for spectrum sensing in cognitive radio networks, International Journal of Electronics Letters, Vol. 1, No. 1, pp. 24–32, 2013.

    Article  Google Scholar 

  12. D. M. Martínez and Á. G. Andrade, “Reducing the effects of the noise uncertainty in energy detectors for cognitive radio networks,” International Journal of Communication Systems, Dec., 2014. doi:10.1002/dac.2907.

    Google Scholar 

  13. N. Armi, B.A.W Chaeriah, and M. Arhad, “Performance Evaluation of Energy Detector in Cognitive Radio System,” Advanced Science Letters, Vol. 21, No. 11, pp. 3494-3496, Nov. 2015.

  14. I. S. Gradshteyn and I. M. Ryzhik, “Table of Integrals, Series, and Products,” 7th ed., Academic Press, 2007.

  15. M. Abdulsattar and Z. Hussein, Energy Detector with Baseband Sampling for Cognitive Radio: Real-Time Implementation, Wireless Engineering and Technology, Vol. 3, No. 4, pp. 229–239, 2012.

    Article  Google Scholar 

  16. P. K. Varshney, Distributed detection and data fusion, Springer-VerlagNew York, 1997.

    Book  Google Scholar 

  17. A. Ghasemi and E. Sousa, “Collaborative spectrum sensing for opportunistic access in fading environments,” in Proc. IEEE Symp. on New Frontiers in Dynamic Spectrum Access Networks (DySPAN), pp. 131–136, Nov. 2005.

  18. Y.-C. Liang, Y. Zeng, C. Peh, and A. T. Hoang, “Sensing-throughput tradeoff for cognitive radio networks,” IEEE Trans. on Wireless Communications, vol. 7, pp. 1326–1337, Apr. 2008.

  19. A. A. EI-Saleh, M. Ismail, and M. A. M. Ali, “Optimizing spectrum sensing parameters for local and cooperative cognitive radios,” in 11th International Conference on Advanced Communication Technology (ICACT 2009), (Phoenix Park, Korea), Feb. 2009.

  20. Z. W. Birnbaum, J. D. Esary, and S. C. Saunders, “Multi-component systems and structures and their reliability,” Technometrics, vol. 1, pp. 55–77, Feb. 1961.

Download references

Author information

Authors and Affiliations

  1. Department of Engineering Technology, Missouri Western State University, St. Joseph, MO, 64506, USA

    Shensheng Tang, Andrew Trautman & Hung Nguyen

Authors
  1. Shensheng Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andrew Trautman
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hung Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shensheng Tang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tang, S., Trautman, A. & Nguyen, H. On Energy-Based Quality of Detection (QoD) for Cognitive Radio Sensor Networks. Int J Wireless Inf Networks 23, 214–221 (2016). https://doi.org/10.1007/s10776-016-0313-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10776-016-0313-4

Keywords

Search

Navigation