Blind eigenvalue-based spectrum sensing for cognitive radio networks

Blind eigenvalue-based spectrum sensing for cognitive radio networks

For access to this article, please select a purchase option:

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
IET Communications — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Spectrum sensing for cognitive radio allows a secondary user to detect spectrum ‘holes’ and to opportunistically exploit this space for unlicensed communication. Blind spectrum sensing has the advantage that it does not require any knowledge of the transmitted signal, the channel or the noise-power, which are usually unknown at the receiver. In this study, the simulation and performance results for the maximum–minimum-eigenvalue and energy-minimum-eigenvalue sensing methods are presented for the Nakagami-m fading channel. The simulation and performance results are presented for the maximum-eigenvalue-to-trace method and the arithmetic-to-geometric-mean method together with the analytical expressions for the threshold, probability of detection and probability of false alarm. In addition, another algorithm, maximum-eigenvalue-geometric-mean is proposed and is investigated in terms of the analytical and simulation results for Nakagami-m fading channels. Improved performance is shown compared to the other schemes when the number of samples is decreased and when the number of cooperating users is increased such that the ratio of the latter to the former is positive and less than unity. Analytical expressions are also presented. The eigenvalue detection methods exhibit good performance in noisy environments and are matched by their bounds.


    1. 1)
      • , : `Spectrum policy task force', Technical report ET Docket no. 02-135, November 2002, Washington, DC.
    2. 2)
    3. 3)
      • Mitola, J.: `Cognitive radio for flexible mobile multimedia communications', Proc. IEEE Int. Workshop on Mobile Multimedia Communications (MoMuC), November 1999, San Diego, CA, p. 3–10.
    4. 4)
    5. 5)
    6. 6)
      • J. Ma , G.Y. Li , B.H. Juang . Signal processing in cognitive radio. Proc. IEEE , 5 , 805 - 823
    7. 7)
    8. 8)
      • Sahai, A., Tandra, R., Mishra, S.M., Hoven, N.: `Fundamental design tradeoffs in cognitive radio systems', Proc. First Int. Workshop on Technology and Policy for Accessing Spectrum (TAPAS), 2006.
    9. 9)
      • Cabric, D., Mishra, S.M., Brodersen, R.W.: `Implementation issues in spectrum sensing for cognitive radios', Proc. Signals, Systems and Computers Conf., 2004, 1, p. 772–776.
    10. 10)
    11. 11)
      • P.P. Bhattacharya . A novel opportunistic spectrum access for applications in cognitive radio. Ubiq. Comput. Commun. J. , 2 , 24 - 28
    12. 12)
    13. 13)
    14. 14)
      • Clancy, T.C.: `Dynamic spectrum access in cognitive radio networks', 2006, Doctor of Philosophy, Faculty of the Graduate School of the University of Maryland, College Park.
    15. 15)
    16. 16)
    17. 17)
    18. 18)
      • Ghasemi, A., Sousa, E.S.: `Collaborative spectrum sensing for opportunistic access in fading environments', Proc. IEEE Int. Symp. on New Frontiers in Dynamic Spectrum Access Networks (DySPAN), November 2005, p. 131–136.
    19. 19)
    20. 20)
    21. 21)
      • Visser, F.E., Janssen, G.J.M., Pawelczak, P.: `Multinode spectrum sensing based on energy detection for dynamic spectrum access', Proc. IEEE Vehicular Technology Conf., May 2008, p. 1394–1398.
    22. 22)
    23. 23)
      • Thanayankizil, L., Kailas, A.: `Spectrum sensing techniques (II): receiver detection and interference management', Technical report, 2007.
    24. 24)
      • Herath, S.P., Rajatheva, N., Tellambura, C.: `On the energy detection of unknown deterministic signal over Nakagami channels with selection combining', Proc. IEEE Canadian Conf. on Electrical and Computer Engineering, May 2009, p. 745–749.
    25. 25)
      • Zeng, Y., Liang, Y.-C.: `Maximum-minimum eigenvalue detection for cognitive radio', Proc. IEEE Int. Symp. on Personal, Indoor and Mobile Radio Communications (PIMRC), 2007.
    26. 26)
    27. 27)
      • Zeng, Y., Koh, C.L., Liang, Y.-C.: `Maximum eigenvalue detection: theory and application', Proc. IEEE Int. Conf. on Communications (ICC), May 2008, p. 4160–4164.
    28. 28)
      • Cardoso, L.S., Debbah, M., Bianchi, P.: `Cooperative spectrum sensing using random matrix theory', Proc. Int. Symp. on Wireless Pervasive Computing (ISWPC), May 2008.
    29. 29)
    30. 30)
    31. 31)
      • Zeng, Y., Liang, Y.-C.: `Robust spectrum sensing in cognitive radio', Proc. IEEE Int. Symp. on Personal, Indoor and Mobile Radio Communications (PIMRC), 2010.
    32. 32)
      • Zeng, Y., Liang, Y.-C.: `Covariance based signal detections for cognitive radio', Proc. Second IEEE Int. Symp. on New Frontiers in Dynamic in Dynamic Spectrum Access Networks (DySPAN), April 2007, p. 202–207.
    33. 33)
    34. 34)
      • Lim, T.J., Zhang, R., Liang, Y.-C., Zeng, Y.: `GLRT-based spectrum sensing for cognitive radio', Proc. IEEE Global Telecommunications Conf. (GLOBECOM), December 2008, p. 1–5.
    35. 35)
    36. 36)
      • R.B. Nelsen . Proofs without words: exercises in visual thinking. Math. Assoc. Am. , 49 - 55
    37. 37)
    38. 38)
      • Rojas, M.A., Lagunas, M.A., Perez, A.I.: `Candidate spectral estimation for cognitive radio', Proc. Int. Conf. on Communication, WSEAS, July 2007, p. 190–197.
    39. 39)
      • Kortun, A., Ratnarajah, T., Sellathurai, M., Zhong, C.: `On the performance of eigenvalue-based spectrum sensing for cognitive radio', Proc. IEEE Symp. on New Frontiers in Dynamic Spectrum Access Networks (DySPAN), April 2010, p. 1–6.
    40. 40)
    41. 41)
    42. 42)
      • Penna, F., Garello, R., Figlioli, D., Spirito, M.A.: `Exact non-asymptotic threshold for eigenvalue-based spectrum sensing', Proc. Fourth Int. Conf. on Cognitive Radio Oriented Wireless Networks and Communications (CROWNCOM), June 2009, p. 1–5.

Related content

This is a required field
Please enter a valid email address