Skip to main content
SpringerLink
Log in

Performance Analysis of Spectrum Sensing Thresholding Methods for Cognitive Radio Networks

  • Conference paper
  • First Online:
Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2020 (AISI 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1261))

  • 2974 Accesses

Abstract

Cognitive radio (CR) is an innovated solution for the scarcity of spectrum bandwidth. Spectrum sensing is a pivotal process to facilitate CR. Spectrum sensing indicates the availability/absence of the primary user (PU) which helps secondary users (SUs) accessing the spectrum band when it is idle while avoiding any interference. This leads to an efficient use for the spectrum. At a low signal-to-noise ratio (SNR), noise fluctuations (i.e., noise uncertainty) is the main reason for missed detection or false alarm; which results in higher interference. This paper introduces an efficient adaptive detection scheme for CR networks, where Various SUs participate to distinguish inactive spectrum bands; improving the detection’s efficiency and overcoming the interference by decreasing the error probability in spectrum sensing, and overcoming node failure using fusion center technique. Monte Carlo is used to analyze the efficiency of detection under the usage of single, and adaptive double thresholds.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Al-Jarrah, M.A., Al-Dweik, A., Ikki, S.S., Alsusa, E.: Spectrum-occupancy aware cooperative spectrum sensing using adaptive detection. IEEE Syst. J. 1–12 (2019, in press). https://doi.org/10.1109/JSYST.2019.2922773

  2. Ali, A., Hamouda, W.: Advances on spectrum sensing for cognitive radio networks: theory and applications. IEEE Commun. Surv. Tutor. 19(2), 1277–1304 (2017)

    Article  Google Scholar 

  3. Arjoune, Y., El Mrabet, Z., El Ghazi, H., Tamtaoui, A.: Spectrum sensing: enhanced energy detection technique based on noise measurement. In: 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC), pp. 828–834. IEEE (2018)

    Google Scholar 

  4. Atapattu, S., Tellambura, C., Jiang, H.: Energy Detection for Spectrum Sensing in Cognitive Radio. Springer, Cham (2014)

    Google Scholar 

  5. Bunch, J.R., Fierro, R.D.: A constant-false-alarm-rate algorithm. Linear Algebra Appl. 172, 231–241 (1992)

    Article  Google Scholar 

  6. Charan, C., et al.: Double threshold based cooperative spectrum sensing with consideration of history of sensing nodes in cognitive radio networks. In: 2018 2nd International Conference on Power, Energy and Environment: Towards Smart Technology (ICEPE), pp. 1–9. IEEE (2018)

    Google Scholar 

  7. Elshishtawy, R.M., Eldien, A.S.T., Fouda, M.M., Eldeib, A.H.: Implementation of multi-channel energy detection spectrum sensing technique in cognitive radio networks using LabVIEW on USRP-2942R. In: 2019 15th International Computer Engineering Conference (ICENCO), pp. 1–6. IEEE (2019)

    Google Scholar 

  8. Fadlullah, Z.M., Nishiyama, H., Kato, N., Fouda, M.M.: Intrusion detection system (IDS) for combating attacks against cognitive radio networks. IEEE Netw. 27(3), 51–56 (2013)

    Article  Google Scholar 

  9. Fouda, M.A., Eldien, A.S.T., Mansour, H.A.: FPGA based energy detection spectrum sensing for cognitive radios under noise uncertainty. In: 2017 12th International Conference on Computer Engineering and Systems (ICCES), pp. 584–591. IEEE (2017)

    Google Scholar 

  10. Ghasemi, A., Sousa, E.S.: Collaborative spectrum sensing for opportunistic access in fading environments. In: First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN), pp. 131–136. IEEE (2005)

    Google Scholar 

  11. Ghazizadeh, E., Abbasi-moghadam, D., Nezamabadi-pour, H.: An enhanced two-phase SVM algorithm for cooperative spectrum sensing in cognitive radio networks. Int. J. Commun Syst 32(2), e3856 (2019)

    Article  Google Scholar 

  12. Gorcin, A., Qaraqe, K.A., Celebi, H., Arslan, H.: An adaptive threshold method for spectrum sensing in multi-channel cognitive radio networks. In: 2010 17th International Conference on Telecommunications, pp. 425–429. IEEE (2010)

    Google Scholar 

  13. Hai, W., Zhang, Y., Chen, Z., Guo, X., He, C.: A signal marker method based on double threshold energy detection. In: 2018 12th International Symposium on Antennas, Propagation and EM Theory (ISAPE), pp. 1–4. IEEE (2018)

    Google Scholar 

  14. He, Y., Xue, J., Ratnarajah, T., Sellathurai, M., Khan, F.: On the performance of cooperative spectrum sensing in random cognitive radio networks. IEEE Syst. J. 12(1), 881–892 (2016)

    Article  Google Scholar 

  15. Lee, Y.L., Saad, W.K., El-Saleh, A.A., Ismail, M.: Improved detection performance of cognitive radio networks in AWGN and Rayleigh fading environments. J. Appl. Res. Technol. 11(3), 437–446 (2013)

    Article  Google Scholar 

  16. Liu, X., Zhang, C., Tan, X.: Double-threshold cooperative detection for cognitive radio based on weighing. Wirel. Commun. Mob. Comput. 14(13), 1231–1243 (2014)

    Article  Google Scholar 

  17. Muthumeenakshi, K., Radha, S.: Improved sensing accuracy using enhanced energy detection algorithm with secondary user cooperation in cognitive radios. Int. J. Commun. Netw. Inf. Secur. 6(1), 17–28 (2014)

    Google Scholar 

  18. Niu, R., Chen, B., Varshney, P.K.: Fusion of decisions transmitted over Rayleigh fading channels in wireless sensor networks. IEEE Trans. Signal Process. 54(3), 1018–1027 (2006)

    Article  Google Scholar 

  19. Omer, A.E.: Review of spectrum sensing techniques in cognitive radio networks. In: 2015 International Conference on Computing, Control, Networking, Electronics and Embedded Systems Engineering (ICCNEEE), pp. 439–446. IEEE (2015)

    Google Scholar 

  20. Qin, Z., Wang, J., Chen, J., Wang, L.: Adaptive compressed spectrum sensing based on cross validation in wideband cognitive radio system. IEEE Syst. J. 11(4), 2422–2431 (2015)

    Article  Google Scholar 

  21. Ranjan, A., Singh, B., et al.: Design and analysis of spectrum sensing in cognitive radio based on energy detection. In: 2016 International Conference on Signal and Information Processing (IConSIP), pp. 1–5. IEEE (2016)

    Google Scholar 

  22. Sarala, B., Devi, S.R., Sheela, J.J.J.: Spectrum energy detection in cognitive radio networks based on a novel adaptive threshold energy detection method. Comput. Commun. 152, 1–7 (2020)

    Article  Google Scholar 

  23. Tandra, R., Sahai, A.: SNR walls for signal detection. IEEE J. Sel. Top. Signal Process. 2(1), 4–17 (2008)

    Article  Google Scholar 

  24. Umebayashi, K., Hayashi, K., Lehtomäki, J.J.: Threshold-setting for spectrum sensing based on statistical information. IEEE Commun. Lett. 21(7), 1585–1588 (2017)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Faculty of Engineering at Shoubra, Benha University, Benha, Egypt

    Rhana M. Elshishtawy, Adly S. Tag Eldien & Mostafa M. Fouda

  2. Department of Electrical and Communication Engineering, Canadian International College for Engineering, Cairo, Egypt

    Ahmed H. Eldeib

Authors
  1. Rhana M. Elshishtawy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Adly S. Tag Eldien
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mostafa M. Fouda
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ahmed H. Eldeib
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rhana M. Elshishtawy .

Editor information

Editors and Affiliations

  1. Faculty of Computers and Artificial Intelligence, Information Technology Department, and Chair of the Scientific Research Group in Egypt, Cairo University, Cairo, Egypt

    Aboul Ella Hassanien

  2. Department of Electronics and Computer Science, Koszalin University of Technology, Koszalin, Poland

    Adam Slowik

  3. Faculty of Electrical Engineering and Computer Science, VĹ B-Technical University of Ostrava, Ostrava-Poruba, Moravskoslezsky, Czech Republic

    Václav Snášel

  4. Rector of the Electronic Research Institute, Cairo, Egypt

    Hisham El-Deeb

  5. Faculty of computers and information, Ain Shams University, Cairo, Egypt

    Fahmy M. Tolba

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Elshishtawy, R.M., Eldien, A.S.T., Fouda, M.M., Eldeib, A.H. (2021). Performance Analysis of Spectrum Sensing Thresholding Methods for Cognitive Radio Networks. In: Hassanien, A.E., Slowik, A., Snášel, V., El-Deeb, H., Tolba, F.M. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2020. AISI 2020. Advances in Intelligent Systems and Computing, vol 1261. Springer, Cham. https://doi.org/10.1007/978-3-030-58669-0_43

Download citation

Publish with us

Policies and ethics

Search

Navigation