Skip to main content
SpringerLink
Log in

An Efficient Composite Two-Tier Threshold Cooperative Spectrum Sensing Technique for 5G Systems

  • Research Article-Electrical Engineering
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

There are fixed thresholds in the conventional double threshold sensing techniques. There is no sensing or confusion region when the test statistic lies between high and low thresholds leading to meager detection probability and lengthier sensing time for the upcoming fifth-generation networks. Hence, there occur problems of low SNR and threshold discrepancies in conventional detectors. So, a novel composite two-tier threshold (CTTT) cooperative spectrum sensing algorithm has been proposed that utilizes the confusion region, and the energy vectors, RSSI vectors, and distance vectors are exploited by the secondary users in a two-tier process to sense the spectrum. Relay centers (RCs) are introduced that can combine the decisions of the secondary users according to different decision strategies and decide about final spectrum availability. The proposed model operates at 2.4 GHz in the sub-6 GHz band of fifth-generation communication. The proposed scheme's performance is assessed on false alarm rate, detection probability, miss detection rate, sensing time, and bit error rate vs. SNR graphs with the conventional methods. The simulated results depict that the proposed scheme outperforms the existing techniques with sensing time as low as 1.09 s, a very low miss detection probability of 0.0002100, and a significantly less BER of 0.0001, giving robust spectrum predictions for the 5G environments.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig.1
Fig.2
Fig.3
Fig.4
Fig.5
Fig.6
Fig.7
Fig.8
Fig.9
Fig.10

Similar content being viewed by others

References

  1. Mitola, J.; Maguire, G.Q.: Cognitive radio: making software radios more personal. IEEE Pers. Commn. 6, 13–18 (1999)

    Article  Google Scholar 

  2. Adu Ansere, J.; Han, G.; Wang, H.; Choi, C.; Wu, C.: A reliable energy efficient dynamic spectrum sensing for cognitive radio IoT networks. IEEE Int. of Things J. 6, 6748–6759 (2019)

    Article  Google Scholar 

  3. Kumar, A.; Saha, S.; Tiwari, K.: A double threshold-based cooperative spectrum sensing with novel hard-soft combining over fading channels. IEEE Wireless Commn. Lett. 8, 1154–1158 (2019)

    Article  Google Scholar 

  4. Liu, X.; Sun, C.; Zhou, M.; Wu, C.; Peng, B.; Li, P.: Reinforcement learning-based multislot double threshold spectrum sensing with bayesian fusion for industrial big spectrum data. IEEE Trans. On Indus. Inf. 17, 3391–3400 (2020)

    Article  Google Scholar 

  5. Elias, F.G.M.; Fernández, E.M.G.: An analysis of energy detector based on improved approximations of the chi-square distributions. J. Wireless Com. Netw 44, 1–18 (2021)

    Google Scholar 

  6. Zan, L., Xihong, C. and Zedong, X.: A Novel Double-Threshold Energy Detection Based on Correlativity. 2019 IEEE 9th International Conference on Electronics Information and Emergency Communication (ICEIEC), Beijing, China, 151–153 (2019).

  7. Smriti, Chhagan, C.: Double Threshold-Based Energy Detection Spectrum Sensing Scheme by Considering the Sensing History in Confusion Region. 2018 5th International Conference on Signal Processing and Integrated Networks (SPIN), Noida, 518–521 (2018).

  8. Hassan, S.M.; Eltholth, A.; Ammar, A.H.: Double threshold weighted energy detection for asynchronous PU activities in the presence of noise uncertainty. IEEE Access. 8, 177682–177692 (2020)

    Article  Google Scholar 

  9. Bayrakdar, M.E.: Cooperative communication based access technique for sensor networks. International J. of Elec. 107, 212–225 (2020)

    Article  Google Scholar 

  10. Bayrakdar, M.E. and Çalhan, A.: Performance analysis of sensing based spectrum handoff process for channel bonding mechanism in wireless cognitive networks. 2017 25th Signal Processing and Communications Applications Conference (SIU), Antalya, 1–4 (2017).

  11. Bayrakdar, M.E., Atmaca, S. and Karahan, A.: A slotted ALOHA-Based random access cognitive radio network with capture effect in Rayleigh fading channels. 2013 International Conference on Electronics, Computer and Computation (ICECCO), Ankara, Turkey, 72–75 (2013)

  12. Li, C.; Wang, J.; Zheng, F.; Cioffi, J.M.; Yang, L.: Overhearing-based co-operation for two-cell network with asymmetric uplink-downlink Traffics. IEEE Trans. Sig. Inf Proc. over Netw. 2, 350–361 (2016)

    MathSciNet  Google Scholar 

  13. Li, C.; Yang, H.J.; Sun, F.; Cioffi, J.M.; Yang, L.: Multiuser overhearing for cooperative two-way multiantenna relays. IEEE Trans. on Veh. Tech. 65, 3796–3802 (2016)

    Article  Google Scholar 

  14. Li, C.; Zhang, S.; Liu, P.; Sun, F.; Cioffi, J.M.; Yang, L.: Overhearing protocol design exploiting intercell interference in cooperative green networks. IEEE Trans. on Veh. Tech. 65, 441–446 (2016)

    Article  Google Scholar 

  15. Shen, F.; Ding, G.; Wang, Z.; Wu, Q.: UAV-based 3D spectrum sensing in spectrum-heterogeneous networks. IEEE Trans. on Veh. Tech. 68, 5711–5722 (2019)

    Article  Google Scholar 

  16. Lampe, B.; Chang, C.; Bekit, A.; Porta, C.D.: Restricted entropy and spectrum properties for the compressively sensed domain in hyperspectral imaging. IEEE Trans. Geosci. Rem Sens. 58, 5642–5652 (2020)

    Article  Google Scholar 

  17. Yang, L.; Fang, J.; Duan, H.; Li, H.: Fast compressed power spectrum estimation: toward a practical solution for wideband spectrum sensing. IEEE Trans. on Wireless Commn. 19, 520–532 (2020)

    Article  Google Scholar 

  18. Torlak, M.; Namgoong, W.: Spectral detection of frequency-sparse signals: compressed sensing vs sweeping spectrum scanning. IEEE Access 9, 30060–30070 (2021)

    Article  Google Scholar 

  19. Zhang, J.; Liu, L.; Liu, M.; Yi, Y.; Yang, Q.; Gong, F.: MIMO Spectrum sensing for cognitive radio-based internet of things. IEEE Int. of Things J. 7, 8874–8885 (2020)

    Article  Google Scholar 

  20. Yin, W.; Chen, H.: Decision-driven time-adaptive spectrum sensing in cognitive radio networks. IEEE Trans. on Wireless Comm. 19, 2756–2769 (2020)

    Article  Google Scholar 

  21. Huang, H.; Mu, J.; Jing, X.: Cooperative spectrum sensing based on centralized double threshold in MCN. China Commn. 17, 235–242 (2020)

    Google Scholar 

  22. Zhai, S.; Tang, X.; Huang, T.; Zhao, X.; Wu, Y.: A double threshold cooperative gnss interference detection algorithm based on fuzzy logic. IEEE Access 8, 177053–177063 (2020)

    Article  Google Scholar 

  23. Golvaei, M.; Fakharzadeh, M.: A fast soft decision algorithm for cooperative spectrum sensing. IEEE trans. circ. Sys Exp. Briefs 68, 241–245 (2021)

    Google Scholar 

  24. Li, M., et al.: Censor-based cooperative multi-antenna spectrum sensing with imperfect reporting channels. IEEE Trans. on Sust. Comp. 5, 48–60 (2020)

    Article  Google Scholar 

  25. Mahendru, G.; Shukla, A.; Banerjee, P.: A novel mathematical model for energy detection based spectrum sensing in cognitive radio networks. Wireless Pers Commun. 110, 1237–1249 (2020)

    Article  Google Scholar 

  26. Yadav, P.V.; Alimohammad, A.; Harris, F.: Efficient design and implementation of energy detection-based spectrum sensing. Circuits Syst Signal Process. 38, 5187–5211 (2019)

    Article  Google Scholar 

  27. Bala Vishnu, J.; Bhagyaveni, M.A.: Energy efficient cognitive radio sensor networks with team-based hybrid sensing. Wireless Pers. Commun. 111, 929–945 (2020)

    Article  Google Scholar 

  28. Kumar, A.; Pandit, S.; Singh, G.: Threshold selection analysis of spectrum sensing for cognitive radio network with censoring based imperfect reporting channels. Wireless Netw 27, 961–980 (2021)

    Article  Google Scholar 

  29. Abdullah, H.N., Bakr, N.Sh. and Valenzuela, A.A.: Double threshold soft decision rule for improving performance of cognitive radio networks. 2019 IEEE 9th International Conference on Consumer Electronics (ICCE-Berlin), Berlin, Germany,158–161 (2019).

  30. Soofi S., Potnis A. and Diwivedy P.: Efficient dynamic double threshold energy detection of cooperative spectrum sensing in cognitive radio. Emerging Research in Computing, Information, Communication and Applications. Advances in Intelligent Systems and Computing, 882., Springer, Singapore (2019).

  31. Rappaport, T.: Wireless Communication: Principles and Practice,2ndEd, Prentice-Hall (2002).

  32. HyungSeo, K.; InSoo, K.: Beacon node based localization algorithm using received signal strength (rss) and path loss calibration for wireless sensor networks. J. Inst. Webc Int. Tel. 11, 15–21 (2011)

    Google Scholar 

  33. Taejoon, A.; Insoo, K.: A RSS-based localization for multiple modes using bayesian compressive sensing with path-loss estimation. J. Inst. Web Int. Tel. 12, 29–36 (2012)

    Google Scholar 

  34. Lee, Y.; Koo, I.: A received signal strength-based primary user localization scheme for cognitive radio sensor networks using underlay model-based spectrum access. KSII Trans. on Int. and Inf Sys. 8, 2663–2674 (2014)

    Google Scholar 

  35. Ekin, S.; Abdallah, M.M.; Qaraqe, K.A.; Serpedin, E.: Random subcarrier allocation in OFDM-based cognitive radio networks. IEEE Trans. Signal Process. 60, 4758–4774 (2012)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics & Communication, Indira Gandhi Delhi Technical University, Delhi, 110006, India

    Parnika Kansal & Ashwni Kumar

  2. Department of Electronics & Communication, Netaji Subhas University of Technology (Formerly AIACT&R), East Campus, Delhi, 110031, India

    M Gangadharappa

Authors
  1. Parnika Kansal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M Gangadharappa
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ashwni Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Parnika Kansal.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kansal, P., Gangadharappa, . & Kumar, A. An Efficient Composite Two-Tier Threshold Cooperative Spectrum Sensing Technique for 5G Systems. Arab J Sci Eng 47, 2865–2879 (2022). https://doi.org/10.1007/s13369-021-05938-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-021-05938-4

Keywords

Search

Navigation