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RETRACTED ARTICLE: Classical energy detection method for spectrum detecting in cognitive radio networks by using robust augmented threshold technique

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This article was retracted on 21 December 2022

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Abstract

Spectrum detecting is the essential and crucial mechanisms of cognitive radio (CR) to the invention the unemployed spectrum. CR system has been suggested as a conceivable resolution for enhancing the spectrum use by empowering unprincipled spectrum sharing. The principal prerequisite for enabling CR to utilize authorized range on an optional premise is not making interfering to primary users. The principal goal of CR is to use rare and limited natural resource efficiently with no obstruction to the primary users (PUs). This work presents an overview of CR architecture, discusses the characteristics and benefits of a CR. Energy identification, matched channel filter detection, and cyclostationary recognitions are most conventional techniques for spectrum sensing. The explanation behind picking energy detection procedure, it did not need any previous info from the primary user transmission. Additionally, the particular result of energy detection technique corrupts with a lower sign to noise ratio (SNR) level signal area. General detection performance of energy detection exceptionally depends upon noise, mainly while the SNR is low for PU. To consider this issue, this paper shows a remarkable, augmented threshold model for efficient energy detection procedure to improve the detection execution at low SNR level. The simulation results demonstrate the energy detection performance utilizing proposed system model is excellent than a fixed threshold at low SNR signal areas.

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References

  1. Youssef, M., Ibrahim, M., Abdelatif, M., Chen, L., Vasilakos, A.: Routing metrics of cognitive radio networks: a survey. IEEE Commun. Surv. Tutor. 99, 1–18 (2013)

    Google Scholar 

  2. Abdelaziz, A., ElNainay, M.: Metric-based taxonomy of routing protocols for cognitive radio ad hoc networks. J. Netw. Comput. Appl. 40, 151–163 (2013)

    Article  Google Scholar 

  3. Beltagy, I., Youssef, M., El-Derini, M.: A new routing metric and protocol for multipath routing in cognitive networks. In: IEEE Wireless Communications and Networking Conference (WCNC) pp. 974–979 (2011)

  4. Karmoose, M., Habak, K., ElNainay, M., Youssef, M.: Dead zone penetration protocol for cognitive radio networks. In: 2013 IEEE 9th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), pp. 529–536 (2013)

  5. Chowdhury, K.R., DiFelice, M., Akyildiz, I.F.: Tp-Krahn: a transport protocol for cognitive radio ad-hoc networks. IEEE INFOCOM 2009, 2482–2490 (2009)

    Google Scholar 

  6. Srivastava, V., Motani, M.: Cross-layer design: a survey and the road ahead. IEEE Commun. Mag. 43(12), 112–119 (2005)

    Article  Google Scholar 

  7. Bianchi, G.: Performance analysis of the IEEE 802.11 distributed coordination function. IEEE J. Select. Areas Commun. 18(3), 535–547 (2000)

  8. Mitra, S., Jana, B., Poray, J.: A novel scheme to detect and remove black hole attack in cognitive radio vehicular ad hoc networks(CR-VANETs). In: Computer, Electrical & Communication Engineering (ICCECE) (2016)

  9. Li, B., Li, D., Wu, Q., Li, H.: ASAR: ant-based spectrum aware routing for cognitive radio networks. In: International Conference on Wireless Communications Signal Processing, 2009. WCSP’09, pp. 1–5 (2009)

  10. Chowdhury, K.R., Felice, M.D.: Search: a routing protocol for mobile cognitive radio ad-hoc networks. Comput. Commun. 32(18), 1983–1997 (2009)

    Article  Google Scholar 

  11. Chowdhury, K.R., Di Felice, M.: SEARCH: a routing protocol for mobile cognitive radio ad-hoc networks. In: IEEE Fu Sarnoff Symposium. SARNOFF ’09, pp 1–6 (2009)

  12. Chowdhury, K.R., Akyildiz, I.F.: CRP: a routing protocol for cognitive radio ad hoc net-works. IEEE J. Select. Areas Commun. 29(4), 794–804 (2011)

    Article  Google Scholar 

  13. Caleffi, M., Akyildiz, I.F., Paura, L.: OPERA: optimal routing metric for cognitive radio ad hoc networks. IEEE Trans. Wirel. Commun. 11(8), 2884–2894 (2012)

    Google Scholar 

  14. Dijkstra, E.W.: A note on two problems in connexion with graphs. Numer. Math. 1(1), 269–271 (1959)

    Article  MathSciNet  MATH  Google Scholar 

  15. Bellman, R., Ford, L.: On a routing problem. Q Appl. Math. 1, 87–90 (1958)

    Article  MATH  Google Scholar 

  16. Huang, X.X., Lu, D., Li, P., Fang, Y.: Coolest path: spectrum mobility aware routing metrics in cognitive ad hoc networks. In: 2013 1st International Conference on Fu Distributed Computing Systems (ICDCS), pp. 182–191 (2011)

  17. loannis Pefkianakis, I., Wong, S.H.Y., Lu, Songwu: Samer: Spectrum aware mesh routing in cognitive radio networks. In: 3rd IEEE Symposium on Fu New Frontiers in Dynamic Spectrum Access Networks, 2008. DySPAN, pp. 1–5 (2008)

  18. Sampath, A., Yang, L., Cao, L., Zheng, H., Zhao, B.Y.: High throughput spectrum-aware routing for cognitive radio networks. In: Fu Proc. of International Conference on Cognitive Radio Oriented Wireless Networks and Communications (crown-com) (2007)

  19. Cacciapuoti, A.S., Calcagno, C., Caleffi, M., Paura, L.: CAODV: routing in mobile ad-hoc cognitive radio networks. In: Fu Wireless Days (WD), 2010 IFIP, pp. 1–5 (2010)

  20. Wang, X., Peng, T., Wang, W.: Low-SNR energy detection based on relevance in power density spectrum. In: Proceedings of the 2016 International Conference on Communications, Signal Processing, and Systems

  21. Cacciapuoti, A.S., Caleffi, M., Paura, L.: Reactive routing for mobile cognitive radio ad hoc networks. Ad Hoc Netw. 10(5), 803–815 (2012)

    Article  Google Scholar 

  22. Chatterjee, S., Banerjee, A., Acharya, T., Maity, S.P.: Fuzzy C-Means Clustering in Energy Detection for Cooperative Spectrum Sensing in Cognitive Radio System. Springer International Publishing, Switzerland (2014)

    Book  Google Scholar 

  23. Bogale, T.E., Vandendorpe, L., Le, L.B.: Sensing throughput tradeoff for cognitive radio networks with noise variance uncertainty. In: Cognitive Radio Oriented Wireless Networks and Communications (CROWNCOM) (2014)

  24. So, J., Srikant, R.: Improving channel utilization via cooperative spectrum sensing with opportunistic feedback in cognitive radio networks. IEEE Commun. Lett. 19(6), 1065–1068 (2015)

    Article  Google Scholar 

  25. Doddavenkatappa, M., Chan, M.C., Ananda, A.L.: A dual-radio framework for MAC protocol implementation in wireless sensor networks. In: 2011 IEEE International Conference Communications (ICC)

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Authors and Affiliations

  1. R.M.K Engineering College, Kavaraipettai, 601206, India

    B. Sarala & D. S. Bhargava

  2. R.M.D Engineering College, Kavaraipettai, 601206, India

    D. Rukmani Devi

Authors
  1. B. Sarala
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  2. D. Rukmani Devi
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  3. D. S. Bhargava
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Correspondence to B. Sarala.

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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s10586-022-03936-1

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Sarala, B., Devi, D.R. & Bhargava, D.S. RETRACTED ARTICLE: Classical energy detection method for spectrum detecting in cognitive radio networks by using robust augmented threshold technique. Cluster Comput 22 (Suppl 5), 11109–11118 (2019). https://doi.org/10.1007/s10586-017-1311-8

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  • DOI: https://doi.org/10.1007/s10586-017-1311-8

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