Skip to main content

Advertisement

Log in

Double Threshold Energy Based Spectrum Sensing with Copulas

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

As it is known, Energy based detection is widely used in Cognitive Radio and radar systems. In order to reduce the disruptive effects of wireless channels, Energy based sensing method has been used with different combinations. In this study, a new spectrum sensing method is proposed by combining the dual threshold energy based sensing method with Copula functions. The spectrum sensing model is given for the proposed method and the sensing method is explained with theoretical analysis. For simulation studies, OFDM based communication system was used under Rayleigh fading channel. Performance analysis of the proposed method was compared with traditional Energy based detection (both single and double threshold) and a noticeable increase in performance was observed.

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
Fig. 6

Similar content being viewed by others

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Code Availability

The processed code required to reproduce these findings cannot be shared at this time as the code also forms part of an ongoing study.

References

  1. Bouali, F., Sallent, O., Pérez-Romero, J., et al. (2013). Erratum to: A Fittingness Factor-Based Spectrum Management Framework for Cognitive Radio Networks. Wireless Personal Communications, 73, 1343. https://doi.org/10.1007/s11277-013-1177-x

    Article  Google Scholar 

  2. Ilgin, F. Y. (2020). Energy-based spectrum sensing with copulas for cognitive radios. Bulletin of the Polish Academy of Sciences. Technical Sciences, 68(4), 829–834.

    Google Scholar 

  3. Liu, S. Q., Hu, B. J., & Wang, X. Y. (2012). Hierarchical cooperative spectrum sensing based on double thresholds energy detection. IEEE Communications Letters, 16(7), 1096–1099.

    Article  Google Scholar 

  4. Kumar, A., Thakur, P., Pandit, S., & Singh, G. (2019). Analysis of optimal threshold selection for spectrum sensing in a cognitive radio network: an energy detection approach. Wireless Networks, 25(7), 3917–3931.

    Article  Google Scholar 

  5. Ma, L., Gulliver, T. A., Zhao, A., Ge, C., & Bi, X. (2019). Underwater broadband source detection using an acoustic vector sensor with an adaptive passive matched filter. Applied Acoustics, 148, 162–174.

    Article  Google Scholar 

  6. Kabeel, A. A., Hussein, A. H., Khalaf, A. A., & Hamed, H. F. (2019). A utilization of multiple antenna elements for matched filter based spectrum sensing performance enhancement in cognitive radio system. AEU-International Journal of Electronics and Communications, 107, 98–109.

    Google Scholar 

  7. Dayana, R., Malarvezhi, P., Vadivukkarasi, K., & Kumar, R. (2020). UF0MC-IOTA based cognitive radio transceiver. Wireless Personal Communications. https://doi.org/10.1007/s11277-020-07467-z

    Article  Google Scholar 

  8. Mahendru, G., Shukla, A., & Banerjee, P. (2020). A novel mathematical model for energy detection based spectrum sensing in cognitive radio networks. Wireless Personal Communications, 110(3), 1237–1249.

    Article  Google Scholar 

  9. Ciflikli, C., & Ilgin, F. Y. (2020). Studentized extreme eigenvalue based double threshold spectrum sensing under noise uncertainty. Tehnički vjesnik, 27(2), 353–357.

    Google Scholar 

  10. Çiflikli, C., & Ilgin, F. Y. (2018). Covariance based spectrum sensing with studentized extreme eigenvalue. Tehnički vjesnik, 25(1), 100–106.

    Google Scholar 

  11. Bollig, A., Disch, C., Arts, M., & Mathar, R. (2017). SNR walls in eigenvalue-based spectrum sensing. EURASIP Journal on Wireless Communications and Networking, 2017(1), 109.

    Article  Google Scholar 

  12. Çiflikli, C., & Ilgin, F. Y. (2020). Multiple antenna spectrum sensing based on glr detector in cognitive radios. Wireless Personal Communications, 110(4), 1915–1927.

    Article  Google Scholar 

  13. Zhang, S., Wang, Y., Li, J., Wan, P., Zhang, Y., & Li, N. (2019). A cooperative spectrum sensing method based on information geometry and fuzzy c-means clustering algorithm. EURASIP Journal on Wireless Communications and Networking, 2019(1), 17.

    Article  Google Scholar 

  14. Paul, A., & Maity, S. P. (2016). Kernel fuzzy c-means clustering on energy detection based cooperative spectrum sensing. Digital Communications and Networks, 2(4), 196–205.

    Article  Google Scholar 

  15. Dibal, P. Y., Onwuka, E. N., Agajo, J., & Alenoghena, C. O. (2018). Application of wavelet transform in spectrum sensing for cognitive radio: A survey. Physical Communication, 28, 45–57.

    Article  Google Scholar 

  16. Hosseini, H., Anpalagan, A., Raahemifar, K., Erkucuk, S., & Habib, S. (2016). Joint wavelet-based spectrum sensing and FBMC modulation for cognitive mmWave small cell networks. IET Communications, 10(14), 1803–1809.

    Article  Google Scholar 

  17. Patel, D. K., Soni, B., & López-Benítez, M. (2019). Improved likelihood ratio statistic-based cooperative spectrum sensing for cognitive radio. IET Communications, 14(11), 1675–1686.

    Article  Google Scholar 

  18. Yang, Y., Chen, J. I., & Liu, J. (2003). Ordered Statistic Analysis for Diversity Combining Schemes over Nakagami-m Fading Channel. Wireless Personal Communications, 24, 463–481. https://doi.org/10.1023/A:1023223625766

    Article  Google Scholar 

  19. Develi, I. (2020). Spectrum sensing in cognitive radio networks: threshold optimization and analysis. EURASIP Journal on Wireless Communications and Networking, 2020(1), 1–19.

    Google Scholar 

  20. Dannana, S., Chapa, B. P., & Rao, G. S. (2018). Spectrum sensing using matched filter detection. In Intelligent Engineering Informatics (pp. 497–503). Singapore: Springer.

  21. Kumar, A., & NandhaKumar, P. (2019). OFDM system with cyclostationary feature detection spectrum sensing. ICT Express, 5(1), 21–25.

    Article  Google Scholar 

  22. Mehrabian, A., & Zaimbashi, A. (2018). Robust and blind eigenvalue-based multiantenna spectrum sensing under IQ imbalance. IEEE Transactions on Wireless Communications, 17(8), 5581–5591.

    Article  Google Scholar 

  23. Lei, K. J., Tan, Y. H., Yang, X., & Wang, H. R. (2018). A K-means clustering based blind multiband spectrum sensing algorithm for cognitive radio. Journal of Central South University, 25(10), 2451–2461.

    Article  Google Scholar 

  24. Bishnu, A., & Bhatia, V. (2018). LogDet covariance based spectrum sensing under colored noise. IEEE Transactions on Vehicular Technology, 67(7), 6716–6720.

    Article  Google Scholar 

  25. Lee, W., Kim, M., & Cho, D. H. (2019). Deep cooperative sensing: Cooperative spectrum sensing based on convolutional neural networks. IEEE Transactions on Vehicular Technology, 68(3), 3005–3009.

    Article  Google Scholar 

  26. Cichoń, K., Kliks, A., & Bogucka, H. (2016). Energy-efficient cooperative spectrum sensing: A survey. IEEE Communications Surveys and Tutorials, 18(3), 1861–1886.

    Article  Google Scholar 

  27. Jia, M., Liu, X., Yin, Z., Guo, Q., & Gu, X. (2017). Joint cooperative spectrum sensing and spectrum opportunity for satellite cluster communication networks. Ad Hoc Networks, 58, 231–238.

    Article  Google Scholar 

  28. Hosni, I., & Hamdi, N. (2017). Distributed cooperative spectrum sensing with wireless sensor network cluster architecture for smart grid communications. International Journal of Sensor Networks, 24(2), 118–124.

    Article  Google Scholar 

  29. Sun, M., Zhao, C., Yan, S., & Li, B. (2016). A novel spectrum sensing for cognitive radio networks with noise uncertainty. IEEE Transactions on Vehicular Technology, 66(5), 4424–4429.

    Google Scholar 

  30. Kaya, V., Tuncer, S., & Baran, A. (2021). Detection and classification of different weapon types using deep learning. Applied Sciences, 11(16), 7535.

    Article  Google Scholar 

  31. Rakovic, V., Atanasovski, V., & Gavrilovska, L. (2014). Optimal cooperative spectrum sensing under faded and bandwidth limited control channels. Wireless Personal Communications, 78, 1645–1666. https://doi.org/10.1007/s11277-014-1904-y

    Article  Google Scholar 

  32. Sundaresan, A., Varshney, P. K., & Rao, N. S. (2011). Copula-based fusion of correlated decisions. IEEE Transactions on Aerospace and Electronic Systems, 47(1), 454–471.

    Article  Google Scholar 

  33. Zhang, S., Theagarajan, L. N., Choi, S., & Varshney, P. K. (2019). Fusion of correlated decisions using regular vine copulas. IEEE Transactions on Signal Processing, 67(8), 2066–2079.

    Article  MathSciNet  Google Scholar 

  34. Javadi, S. H., Mohammadi, A., & Farina, A. (2019). Hierarchical copula-based distributed detection. Signal Processing, 158, 100–106.

    Article  Google Scholar 

Download references

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Fatih Yavuz Ilgin.

Ethics declarations

Conflict of interest

The authors have not disclosed any competing interests.

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

Ilgin, F.Y. Double Threshold Energy Based Spectrum Sensing with Copulas. Wireless Pers Commun 126, 2937–2948 (2022). https://doi.org/10.1007/s11277-022-09845-1

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-022-09845-1

Keywords

Navigation