Skip to main content

Advertisement

Log in

Performance of energy detector in the presence of noise uncertainty in cognitive radio networks

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

Energy detector is simple in structure and easy to implement. Therefore, it is a promising candidate for spectrum sensing in cognitive radio networks. However, its detection performance is typically challenged by the noise uncertainty. Thus, the detection performance of energy detector in the presence of noise uncertainty needs to be evaluated. In this paper, we derive the decision rules for the energy detector in the presence of noise uncertainty by employing a widely used model. Firstly, we derive the decision rule for unknown deterministic signal when the noise power is uncertain. Second, we derive the decision rule for random Gaussian distributed signal when there is noise uncertainty. Then, we analyze the detection performance of the energy detector in the presence of noise uncertainty for both unknown deterministic signal and random Gaussian distributed signal. Both theoretical analyses and simulation results show that in the presence of noise uncertainty, our derived decision rules provide precise sensing thresholds for the energy detector. Furthermore, compared with the conventional decision rule obtained by overestimating the noise power, our decision rules provide performance gains in terms of signal to noise ratio.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

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). http://www.fcc.gov/sptf/headlines2004.html.

  3. Zeng, Y., Liang, Y., Lei, Z., et al. (2010). Worldwide regulatory and standardization activities on cognitive radio. In: DySPAN (pp. 1–9). Singapore.

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

    Article  Google Scholar 

  5. Tani, A., & Fantacci, R. (2010). A low-complexity cyclostationary-based spectrum sensing for UWB and WiMAX coexistence with noise uncertainty. IEEE Transactions on Vehicular Technology, 59, 2940–2950.

    Article  Google Scholar 

  6. Yin, W., & Ren, P. (2010). A suboptimal spectrum sensing scheme for OFDM signal in cognitive radios. In: Proceedings of IEEE GLOBECOM (pp. 1–6). Miami, LAX, USA.

  7. Zeng, Y., & Liang, Y. (2009). Spectrum-sensing algorithms for cognitive radio based on statistical covariances. IEEE Transactions on Vehicular Technology, 58, 1804–1815.

    Article  Google Scholar 

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

    Article  Google Scholar 

  9. Digham, F. F., Alouini, M. S., & Simon, M. K. (2007). On the energy detection of unknown signals over fading channels. IEEE Transactions on Communications, 55, 21–24.

    Article  Google Scholar 

  10. Shen, J., Jiang, T., & Zhang, Z. (2009). Maximum channel throughput via cooperative spectrum sensing in cognitive radio networks. IEEE Transactions on Wireless Communications, 9, 5166–5175.

    Article  Google Scholar 

  11. Sung, C. K., & Collings, I. B. (2010). Spectrum sensing technique for cognitive radio systems with selection diversity. In: IEEE GLOBECOM (pp. 1–5). Miami, LAX, USA.

  12. Atapattu, S., Tellambura, C., & Jiang, H. (2010). Analysis of area under the ROC curve of energy detection. IEEE Transactions on Wireless Communications, 9, 1216–1225.

    Article  Google Scholar 

  13. Zhao, C., & Kwak, K. (2010). Joint Sensing time and power allocation in cooperatively cognitive networks. IEEE Communications Letters, 14, 163–165.

    Article  Google Scholar 

  14. Chen, Y. (2010). Analytical performance of collaborative spectrum sensing using censored energy detection. IEEE Transactions on Wireless Communications, 9, 3856–3865.

    Article  Google Scholar 

  15. Joshi, D. R., Popescu, D. C., & Dobre, O. A. (2010). Gradient-based threshold adaption for energy detector in cognitive radio systems. IEEE Communications Letters, 15, 19–21.

    Article  Google Scholar 

  16. Sonnenschein, A., & Fishman, P. M. (1992). Radiometric detection of spread-spectrum signals in noise of uncertain power. IEEE Transactions on Aerospace and Electronic Systems, 28, 654–660.

    Article  Google Scholar 

  17. Zeng, Y., & Liang, Y. (2009). Eigenvalue-based spectrum sensing algorithms for cognitive radio. IEEE Transactions on Communications, 57, 1784–1793.

    Article  Google Scholar 

  18. Wang, P., Fang, J., Han, N., et al. (2010). Multi-Antenna assisted spectrum sensing for cognitive radio. IEEE Transactions on Vehicular Technology, 59, 1791-1800.

    Article  Google Scholar 

  19. Abbas, T., Masoumeh, N. K., & Saeed, G. (2010). Multiple antenna spectrum sensing in cognitive radios. IEEE Transactions on Wireless Communications, 9, 814-823.

    Article  Google Scholar 

  20. Lim, T. J., Zhang, R., Liang, Y., et al. (2010). Multi-antenna based spectrum sensing for cognitive radios: A GLRT approach. IEEE Transactions on Communications, 58, 84-88.

    Article  Google Scholar 

  21. Hamdi, K., Zeng, X. N., Ghrayeb, A., et al. (2010). Impact of noise power uncertainty on cooperative spectrum sensing in cognitive radio systems. In: IEEE GLOBECOM (pp. 1–5). Miami, LAX, USA.

  22. Proakis, J. G., Zhang, J., Zhang, Z., Zheng, B., & Digital Communications. (2009). Fourth, Peking, publishing house of electronic industry.

  23. Kapinas, V. M., Mihos, S. K., & Karagiannidis, G. K. (2009). On the monotonicity of the generalized Marcum and Nuttall Q-functions. IEEE Transactions on Informational Theory, 55, 3701-3710.

    Article  MathSciNet  Google Scholar 

  24. Moghimi, F., Nasri, A., & Schober, R. (2009). Lp-norm spectrum sensing for cognitive radio networks impaired by Non-Gaussian noise. In: IEEE GLOBECOM (pp. 1–5). Honolulu, Hawaii, USA.

  25. Kolodziejski, K. R., & Betz, J. W. (2000). Detection of weak random signals in IID Non-Gaussian noise. IEEE Transactions on Communications, 48, 222–230.

    Google Scholar 

  26. Miller, J. H., & Thomas, J. B. (1972). Detectors for discrite-time signals in Non-Gaussian noise. IEEE Transactions on Information Theory, 18, 241–250.

    Google Scholar 

Download references

Acknowledgments

The paper is supported by National Hi-Tech Research and Development Plan of China under Grant 2009AA011801, it is also supported by National Natural Science Foundation of China under Grant 60832007.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Pinyi Ren.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Yin, W., Ren, P., Cai, J. et al. Performance of energy detector in the presence of noise uncertainty in cognitive radio networks. Wireless Netw 19, 629–638 (2013). https://doi.org/10.1007/s11276-012-0491-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-012-0491-7

Keywords

Navigation