Wireless Networks

, Volume 25, Issue 4, pp 1949–1964 | Cite as

Spectrum sensing exploiting the maximum value of power spectrum density in wireless sensor network

  • Yulong GaoEmail author
  • Yanping Chen


Spectrum sensing plays a foundational role in cognitive radio sensor networks. However, only the methods with low computational complexity can be utilized due to energy restriction of sensor node. To this end, a novel frequency-domain spectrum sensing method is presented to satisfy corresponding requirements of cognitive radio sensor networks. Only the maximum value of power spectrum density is utilized as test statistic to reduce the computational complexity. According to the dependence of 2L real parts and imaginary parts of the maximum value of power spectrum density, we model the maximum value of power spectrum density as the central Chi-square distribution for the \(H_0\) case and non-central Chi-square distribution for the \(H_1\) case. Exploiting resulting distributions, we derive the analytic expressions for the detection probability and the false-alarm probability. Additionally, the computational complexity of the proposed method is quantitatively analyzed. Finally, we certify the proposed test statistic and the probability distribution of the maximum value of power spectrum density and evaluate the impact of some parameters on the detection performance experimentally. The theoretical analysis and simulation results demonstrate that the proposed algorithm can offer high performance gains over the existing time-domain detection method.


Cognitive radio sensor network Frequency-domain spectrum sensing The maximum value of power spectrum density Welch method 



This work is supported by National Natural Science Foundation of China (NSFC) (61671176). We would like to thank Linxiao Su for his suggestion, discussion and simulation code.


  1. 1.
    Wang, B., & Liu, K. J. R. (2011). Advances in cognitive radio networks: A survey. IEEE Journal of Selected Topics in Signal Processing, 5(1), 5–23.CrossRefGoogle Scholar
  2. 2.
    Mitola, J., & Maguire, G, Jr. (1999). Cognitive radio: Making software radio more personal. IEEE Personal Communications, 9(6), 13–18.CrossRefGoogle Scholar
  3. 3.
    Haykin, S. (2005). Cognitive radio brain-empowered wireless communications. IEEE Journal on Selected Areas in Communications, 23(2), 201–220.CrossRefGoogle Scholar
  4. 4.
    Yucek, T., & Arslan, H. (2009). A survey of spectrum sensing algorithms for cognitive radio applications. IEEE Communications Surveys and Tutorials, 11(1), 116–130.CrossRefGoogle Scholar
  5. 5.
    Axell, E., Leus, G., Larsson, E. G., et al. (2012). Spectrum sensing for cognitive radio: State-of-the-art and recent advances. IEEE Signal Processing Magazine, 29(3), 101–116.CrossRefGoogle Scholar
  6. 6.
    Akyildiz, I. F., Lee, W. Y., & Chowdhury, K. R. (2009). CRAHNs: Cognitive radio AD Hoc networks. Ad Hoc Networks, 7(5), 810–836.CrossRefGoogle Scholar
  7. 7.
    Akan, O., Karli, O., & Ergul, O. (2009). Cognitive radio sensor networks. IEEE Network, 23(4), 34–40.CrossRefGoogle Scholar
  8. 8.
    Monemian, M., Mahdavi, M., & Omidi, M. (2016). Optimum sensor selection based on energy constraints in cooperative spectrum sensing for cognitive radio sensor networks. IEEE Sensors Journal, 16(6), 1829–1841.CrossRefGoogle Scholar
  9. 9.
    Baradkar, H. M., & Akojwar, S. G. (2014). Implementation of energy detection method for spectrum sensing in cognitive radio based embedded wireless sensor network node. In 2014 International conference on electronic systems, signal processing and computing technologies (ICESC) (pp. 490–495).Google Scholar
  10. 10.
    Saberali, S. A., & Beaulieu, N. C. (2014). Matched-filter detection of the presence of MPSK signals. In 2014 International symposium on information theory and its applications (ISITA) (pp. 85–89).Google Scholar
  11. 11.
    Zhi, T., Tafesse, Y., & Sadle, B. M. (2012). Cyclic feature detection with sub-nyquist sampling for wideband spectrum sensing. IEEE Journal of Selected Topics in Signal Processing, 6(1), 58–69.CrossRefGoogle Scholar
  12. 12.
    Sedighi, S., Taherpour, A., Khattab, T., & Hasna, M. O.(2014). Multiple antenna cyclostationary-based detection of primary users with multiple cyclic frequency in cognitive radios. In Globecom 2014-cognitive radio and networks symposium (pp. 799–804).Google Scholar
  13. 13.
    Zeng, Y., & Liang, Y.-C. (2009). Eigenvalue-based spectrum sensing algorithms for cognitive radio. IEEE Transactions on Communications, 57(6), 1784–1793.CrossRefGoogle Scholar
  14. 14.
    Shakir, M. Z., Rao, A., & Alouini, M.-S. (2013). Generalized mean detector for collaborative spectrum sensing. IEEE Transactions on Communications, 61(4), 1242–1253.CrossRefGoogle Scholar
  15. 15.
    Sharma, S. K., Chatzinotas, S., & Ottersten, B. (2013). Eigenvalue-based sensing and SNR estimation for cognitive radio in presence of noise correlation. IEEE Transactions on Vehicular Technology, 62(8), 3671–3684.CrossRefGoogle Scholar
  16. 16.
    Mustapha, I., Ali, B. M., Sali, A., & Rasid, M. F. A.(2014). Energy-aware cluster based cooperative spectrum sensing for cognitive radio sensor networks. In 2014 IEEE 2nd international symposium on telecommunication technologies (ISTT) (pp. 45–50).Google Scholar
  17. 17.
    Huang, X., Fei, H., Jun, W., Chen, H.-H., Wang, G., & Jiang, T. (2015). Intelligent cooperative spectrum sensing via hierarchical dirichlet process in cognitive radio networks. IEEE Journal on Selected Areas in Communications, 33(5), 771–787.CrossRefGoogle Scholar
  18. 18.
    Qu, Z., Xu, Y., & Yin, S. (2014). A novel clustering-based spectrum sensing in cognitive radio wireless sensor networks. 2014 IEEE 3rd international conference on cloud computing and intelligence systems (CCIS) (pp. 695–699).Google Scholar
  19. 19.
    Ergul, O., & Akan, O. B. (2014). Cooperative coarse spectrum sensing for cognitive radio sensor networks. IEEE Wireless Communications & Networking Conference, 23(4), 2055–2060.Google Scholar
  20. 20.
    Matinmikko, M., Sarvanko, H., Mustonen, M., & Mammela, A. (2009). Performance of spectrum sensing using Welch’s periodogram in rayleigh fading channel. In Proceedings of the 4th international conference on CROWNCOM (pp. 1–5).Google Scholar
  21. 21.
    Gismalla, E. H., & Alsusa, E. (2011). Performance analysis of the periodogram-based energy detector in fading channels. IEEE Transactions on Signal Processing, 59(8), 3712–3721.MathSciNetCrossRefzbMATHGoogle Scholar
  22. 22.
    Gismalla, E. H., & Alsusa, E. (2012). On the performance of energy detection using Bartlett’s estimate for spectrum sensing in cognitive radio systems. IEEE Transactions on Signal Processing, 60(7), 3394–3404.MathSciNetCrossRefzbMATHGoogle Scholar
  23. 23.
    Dikmese, E., Ilyas, Z., Sofotasios, P. C., Renfors, M., & Valkama, M. (2017). Sparse frequency domain spectrum sensing and sharing based on cyclic prefix autocorrelation. IEEE Journal on Selected Areas in Communications, 35(1), 159–172.Google Scholar
  24. 24.
    Sabahi, M. F., Masoumzadeh, M., & Forouzan, A. R. (2016). Frequency-domain wideband compressive spectrum sensing. IET Commun., 10(13), 1655–1664.CrossRefGoogle Scholar
  25. 25.
    Simon, M. K. (2006). Probability distributions involving gaussian random variables: A handbook for engineers and scientists. New York: Springer.Google Scholar
  26. 26.
    Proakis, J. G., & Manolakis, D. G. (2007). Digital signal processing (4th ed.). Upper Saddle River: Pearson Prentice Hall.Google Scholar
  27. 27.
    Oppenheim, A. V., & Schafer, R. W. (1999). Discrete-time signal processing. Upper Saddle River: Prentice Hall.zbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Communication Research CenterHarbin Institute of TechnologyHarbinChina
  2. 2.School of Computer and Information EngineeringHarbin University of CommerceHarbinChina

Personalised recommendations