Advertisement

Cluster Computing

, Volume 22, Supplement 5, pp 11109–11118 | Cite as

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

  • B. SaralaEmail author
  • D. Rukmani Devi
  • D. S. Bhargava
Article
  • 231 Downloads

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.

Keywords

Cognitive radios Spectrum sensing Energy detection Augmented threshold method 

References

  1. 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. 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)CrossRefGoogle Scholar
  3. 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)Google Scholar
  4. 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)Google Scholar
  5. 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. 6.
    Srivastava, V., Motani, M.: Cross-layer design: a survey and the road ahead. IEEE Commun. Mag. 43(12), 112–119 (2005)CrossRefGoogle Scholar
  7. 7.
    Bianchi, G.: Performance analysis of the IEEE 802.11 distributed coordination function. IEEE J. Select. Areas Commun. 18(3), 535–547 (2000)Google Scholar
  8. 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)Google Scholar
  9. 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)Google Scholar
  10. 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)CrossRefGoogle Scholar
  11. 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)Google Scholar
  12. 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)CrossRefGoogle Scholar
  13. 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. 14.
    Dijkstra, E.W.: A note on two problems in connexion with graphs. Numer. Math. 1(1), 269–271 (1959)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Bellman, R., Ford, L.: On a routing problem. Q Appl. Math. 1, 87–90 (1958)MathSciNetCrossRefzbMATHGoogle Scholar
  16. 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)Google Scholar
  17. 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)Google Scholar
  18. 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)Google Scholar
  19. 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)Google Scholar
  20. 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 SystemsGoogle Scholar
  21. 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)CrossRefGoogle Scholar
  22. 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)CrossRefGoogle Scholar
  23. 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)Google Scholar
  24. 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)CrossRefGoogle Scholar
  25. 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)Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.R.M.K Engineering CollegeKavaraipettaiIndia
  2. 2.R.M.D Engineering CollegeKavaraipettaiIndia

Personalised recommendations