Remember Me
Or use your Academic/Social account:


Or use your Academic/Social account:


You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.


Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message


Verify Password:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Vien, Quoc Tuan; Nguyen, Huan X.; Nallanathan, Arumugam (2016)
Publisher: The Institution of Engineering and Technology (IET)
Languages: English
Types: Article
Subjects: /dk/atira/pure/subjectarea/asjc/2200/2208, Computer Science Applications, Electrical and Electronic Engineering, /dk/atira/pure/subjectarea/asjc/1700/1706

This study investigates cooperative spectrum sensing (CSS) in cognitive wireless radio networks (CWRNs). A practical system is considered where all channels experience Nakagami-m fading and suffers from background noise. The realisation of the CSS can follow two approaches where the final spectrum decision is based on either only the global decision at fusion centre (FC) or both decisions from the FC and secondary user (SU). By deriving closed-form expressions and bounds of missed detection probability (MDP) and false alarm probability (FAP), the authors are able to not only demonstrate the impacts of the m-parameter on the sensing performance, but also evaluate and compare the effectiveness of the two CSS schemes with respect to various fading parameters and the number of SUs. It is interestingly noticed that a smaller number of SUs could be selected to achieve the lower bound of the MDP rather using all the available SUs while still maintaining a low FAP. As a second contribution, they propose a SU selection algorithm for the CSS to find the optimised number of SUs for lower complexity and reduced power consumption. Finally, numerical results are provided to demonstrate the findings.

  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] S. Haykin, “Cognitive radio: brain-empowered wireless communications,” IEEE J. Sel. Areas Commun., vol. 23, no. 2, pp. 201-220, Feb. 2005.
    • [2] I. Mitola, J. and J. Maguire, G.Q., “Cognitive radio: Making software radios more personal,” IEEE Pers. Commun., vol. 6, no. 4, pp. 13-18, Aug. 1999.
    • [3] T. Yucek and H. Arslan, “A survey of spectrum sensing algorithms for cognitive radio applications,” IEEE Commun. Surveys and Tutorials, vol. 11, no. 1, pp. 116-130, first quarter 2009.
    • [4] G. Ganesan and Y. Li, “Cooperative spectrum sensing in cognitive radio, part I: Two user networks,” IEEE Trans. Wireless Commun., vol. 6, no. 6, pp. 2204-2213, Jun. 2007.
    • [5] W. Zhang and K. Letaief, “Cooperative spectrum sensing with transmit and relay diversity in cognitive radio networks,” IEEE Trans. Wireless Commun., vol. 7, no. 12, pp. 4761-4766, Dec. 2008.
    • [6] K. Ben Letaief and W. Zhang, “Cooperative communications for cognitive radio networks,” Proc. of the IEEE, vol. 97, no. 5, pp. 878-893, May 2009.
    • [7] G. Noh, H. Wang, J. Jo, B.-H. Kim, and D. Hong, “Reporting order control for fast primary detection in cooperative spectrum sensing,” IEEE Trans. Veh. Technol., vol. 60, no. 8, pp. 4058-4063, Oct. 2011.
    • [8] Y. Zou, Y.-D. Yao, and B. Zheng, “Cooperative relay techniques for cognitive radio systems: Spectrum sensing and secondary user transmissions,” IEEE Commun. Mag., vol. 50, no. 4, pp. 98-103, Apr. 2012.
    • [9] Q.-T. Vien, B. G. Stewart, H. Tianfield, and H. X. Nguyen, “Efficient cooperative spectrum sensing for three-hop cognitive wireless relay networks,” IET Commun., vol. 7, no. 2, pp. 119-127, 2013.
    • [10] M. Monemian and M. Mahdavi, “Sensing user selection based on energy constraints in cognitive radio networks,” in Proc. IEEE WCNC 2014, Istanbul, Turkey, Apr. 2014, pp. 3379-3384.
    • [11] A. Cacciapuoti, I. Akyildiz, and L. Paura, “Correlation-aware user selection for cooperative spectrum sensing in cognitive radio ad hoc networks,” IEEE J. Sel. Areas Commun., vol. 30, no. 2, pp. 297-306, Feb. 2012.
    • [12] N. Hasan, W. Ejaz, S. Lee, and H. Kim, “Knapsack-based energy-efficient node selection scheme for cooperative spectrum sensing in cognitive radio sensor networks,” IET Commun., vol. 6, no. 17, pp. 2998-3005, Nov. 2012.
    • [13] Y. Zhou, Z. Zhou, and B. Li, “Sensing nodes selection and data fusion in cooperative spectrum sensing,” IET Commun., vol. 8, no. 13, pp. 2308-2314, Sep. 2014.
    • [14] M. Najimi, A. Ebrahimzadeh, S. Hosseini Andargoli, and A. Fallahi, “Energy-efficient sensor selection for cooperative spectrum sensing in the lack or partial information,” IEEE Sensors J., vol. 15, no. 7, pp. 3807-3818, Jul. 2015.
    • [15] A. Ebrahimzadeh, M. Najimi, S. Andargoli, and A. Fallahi, “Sensor selection and optimal energy detection threshold for efficient cooperative spectrum sensing,” IEEE Trans. Veh. Technol., vol. 64, no. 4, pp. 1565-1577, Apr. 2015.
    • [16] J. Ma, G. Zhao, and Y. Li, “Soft combination and detection for cooperative spectrum sensing in cognitive radio networks,” IEEE Trans. Wireless Commun., vol. 7, no. 11, pp. 4502-4507, Nov. 2008.
    • [17] X. Liu and B. Sirkeci-Mergen, “Group-orthogonal MAC for cooperative spectrum sensing in cognitive radios,” in Proc. IEEE MILCOM 2010, San Jose, CA, USA, Oct. 2010, pp. 1221-1226.
    • [18] A. S. Cacciapuoti, M. Caleffi, L. Paura, and R. Savoia, “Decision maker approaches for cooperative spectrum sensing: Participate or not participate in sensing?” IEEE Trans. Wireless Commun., vol. 12, no. 5, pp. 2445-2457, May 2013.
    • [19] F. F. Digham, M.-S. Alouini, and M. K. Simon, “On the energy detection of unknown signals over fading channels,” IEEE Trans. Commun., vol. 55, no. 1, pp. 21-24, Jan. 2007.
    • [20] I. S. Gradshteyn and I. M. Ryzhik, Table of Integrals, Series, and Products, 7th ed. Academic Press, 2007.
    • [21] H. Shin and J. H. Lee, “On the error probability of binary and M-ary signals in Nakagami-m fading channels,” IEEE Trans. Commun., vol. 52, no. 4, pp. 536-539, Apr. 2004.
  • No related research data.
  • Discovered through pilot similarity algorithms. Send us your feedback.

Share - Bookmark

Cite this article