Performance of ED Based Spectrum Sensing Over α–η–μ Fading Channel

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Abstract

Internet of things contains the hefty number of devices communicating with each other; give rise to the problem of spectrum scarcity. Cognitive radio has emerged as the promising solution to this problem. Spectrum sensing is the important function of cognitive radio and energy detector is the most popular technique used for spectrum sensing. In this paper, the performance of energy detector (ED) over α–η–μ fading channel has been analyzed. The analytical expressions for average probability of detection and average area under the receiver operating characteristics curve (AUC) are derived for the generalized fading channel in terms of the bivariate Fox H-function. The closed-form mathematical expressions for the average probability of detection for cooperative spectrum sensing as well as square law selection diversity reception are derived. The implication of the system parameters on the performance of ED is studied in terms of complimentary receiver operating characteristics and AUC. It is shown that the performance of ED can be improved when cooperation and diversity are employed. The derived results are generic and can be directly used for the performance analysis of η–μ and α–μ fading channels and their special cases. Monte-Carlo simulations are incorporated for validating the accuracy of the derived results.

Keywords

Generalised fading Cognitive radio Receiver operating characteristic Bivariate Fox H-function Cooperative spectrum sensing Diversity reception 

Notes

Acknowledgements

The authors would like to thank the anonymous reviewers for their useful suggestions for improving the presentation of the material in this paper.

References

  1. 1.
    Digham, F. F., Alouini, M. S., & Simon, M. K. (2007). On the energy detection of unknown signals over fading channels. IEEE Transaction on Communications, 55(1), 21–24.CrossRefGoogle Scholar
  2. 2.
    Atapattu, S., Tellambura, C., & Jiang, H. (2010). Analysis of area under the ROC curve energy detection. IEEE Transaction on Wireless Communications, 9(3), 1216–1225.CrossRefGoogle Scholar
  3. 3.
    Fathi, Y., & Tawfik, M. H. (2012). Versatile performance expression for energy detector over α–μ generalised fading channels. Electronics Letters, 48(17), 1081–1082.CrossRefGoogle Scholar
  4. 4.
    Sofotasios, P. C., Rebeiz, E., Zhang, L., Tsiftsis, T. A., Cabric, D., & Freear, S. (2013). Energy detection based spectrum sensing over κ–μ and κ–μ extreme fading channels. IEEE Transaction on Vehicular Technology, 62(3), 1031–1040.CrossRefGoogle Scholar
  5. 5.
    Adebola, E., & Annamalai, A. (2014). Unified analysis of energy detectors with diversity reception in generalised fading channels. IET Communications, 8(17), 3095–3104.CrossRefGoogle Scholar
  6. 6.
    Al-Hmood, H., & Al-Raweshidy, H. S. (2015). Performance analysis of energy detector over η–μ fading channel: PDF-based approach. Electronics Letters, 51(3), 249–251.CrossRefGoogle Scholar
  7. 7.
    Annamalai, A., & Olaluwe, A. (2014). Energy detection of unknown deterministic signals in κ–μ and η–μ generalized fading channels with diversity receivers. In IEEE computing networking and communications, Honolulu, Hawaii, USA (pp. 761–765).Google Scholar
  8. 8.
    Peppas, K. P., Efthymoglou, G., Aalo, V. A., Alwakeel, M., & Alwakeel, S. (2015). Energy detection of unknown signals in Gamma shadowed Rician fading environments with diversity reception. IET Communications, 9(2), 196–210.CrossRefGoogle Scholar
  9. 9.
    Chandrasekaran, G., & Kalyani, S. (2015). Performance analysis of cooperative spectrum sensing over κ–μ shadowed fading. IEEE Wireless Communications Letters, 4(5), 553–556.CrossRefGoogle Scholar
  10. 10.
    Al-Hmood, H., & Al-Raweshidy, H. S. (2017). Analysis of energy detection with diversity receivers over non-identically distributed κ–μ shadowed fading channels. Electronics Letters, 53(2), 83–85.CrossRefGoogle Scholar
  11. 11.
    Al-Hmood, H., & Al-Raweshidy, H. S. (2016). Unified modeling of composite κ–μ/gamma, η–μ/gamma, and α–μ/gamma fading channels using a mixture gamma distribution with applications to energy detection. IEEE Antennas and Wireless Propagation Letters, 16, 104–108.CrossRefGoogle Scholar
  12. 12.
    Papazafeiropoulos, A. K., & Kotsopoulos, S. A. (2011). The α–λ–μ and α–η–μ small-scale general fading distributions: A unified approach. Wireless Personal Communications, 57(4), 735–751.CrossRefGoogle Scholar
  13. 13.
    Sofotasios, P. C., Rebeiz, E., Zhang, L., Tsiftsis, T. A., Cabric, D., & Freear, S. (2015). Entropy and channel capacity under optimum power and rate adaptation over generalized fading conditions. IEEE Signal Processing Letters, 22(11), 2162–2166.CrossRefGoogle Scholar
  14. 14.
    Aldalgamouni, T., Magableh, A. M., Mater, S., & Badarneh, O. S. (2017). Capacity analysis of α–η–μ channels over different adaptive transmission protocols. IET Communications, 11(7), 1114–1122.CrossRefGoogle Scholar
  15. 15.
    Badarneh, O., & Aloqlah, M. (2016). Performance analysis of digital communication systems over generalized α–η–μ fading channels. IEEE Transaction on Vehicular Technology, 65(10), 7972–7981.CrossRefGoogle Scholar
  16. 16.
    Badarneh, O. S. (2015). Error rate analysis of M-ary phase shift keying in α–η–μ fading channels subject to additive Laplacian noise. IEEE Communications Letters, 19(7), 1253–1256.CrossRefGoogle Scholar
  17. 17.
    Souza, R. A. A., Ribeiro, A. M. O., & Guimarães, D. A. (2015). On the efficient generation of α–κ–μ and α–η–μ white samples with applications. International Journal of Antennas and Propagation, 2015, 1–13.CrossRefGoogle Scholar
  18. 18.
    Batista, F. P., Souza, R. A. A, & Ribeiro, A. M. O. (2016). Maximum likelihood estimator for the α–η–μ fading environment. In IEEE radio and wireless symposium (RWS) (pp. 133–136).Google Scholar
  19. 19.
    Prudnikov, A. P., Brychkov, Y. A., & Marichev, O. I. (1990). Integrals and series, volume 3: More special functions. New York: Gordon and Breach Science Publishers.MATHGoogle Scholar
  20. 20.
    Gradshteyn, I. S., & Ryzhik, I. M. (2000). Table of Integrals, Series, and Products (6th ed.). New York: Academic Press.MATHGoogle Scholar
  21. 21.
    Mathai, A. M., Saxena, R. K., & Haubold, H. J. (2010). The H-function theory applications. New York: Springer.MATHGoogle Scholar
  22. 22.
    Duan, D., Yang, L., & Principe, J. (2010). Cooperative diversity of spectrum sensing for cognitive radio systems. IEEE Transaction on Signal Processing, 58(6), 3218–3227.MathSciNetCrossRefGoogle Scholar
  23. 23.
    Letaief, K., & Zhang, W. (2009). Cooperative communications for cognitive radio networks. Proceedings of the IEEE, 97(5), 878–893.CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Central Research LaboratoryBharat Electronics LimitedGhaziabadIndia

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