access icon free Analysis of collaborative spectrum sensing with binary phase shift keying signal power estimation errors

Novel iterative and non-iterative blind estimation methods for binary phase shift keying modulated primary user signal power are proposed using the maximum-likelihood principle for collaborative spectrum sensing. Using these estimators, the performance of three different existing collaborative spectrum sensing methods is compared under realistic scenarios. The effect of the primary user traffic on these methods is also explored. Numerical results show that, unlike the case of no estimation error as examined in this literature, the performance gains of the weighted linear combining and optimal combining methods over the conventional equal gain combining method decrease or even become negative when there is estimation error. They also show that the proposed blind estimation methods offer an excellent trade-off between reliability and complexity.

Inspec keywords: maximum likelihood estimation; phase shift keying; diversity reception; error statistics; radio spectrum management; modulation; telecommunication traffic

Other keywords: primary user traffic effect; weighted linear combining method; binary phase shift keying signal power estimation error; reliability; collaborative spectrum sensing analysis; maximum likelihood principle; noniterative blind estimation method; conventional equal gain combining method; optimal combining method; complexity; primary user signal power modulation

Subjects: Other topics in statistics; Signal detection; Modulation and coding methods; Radio links and equipment

References

    1. 1)
    2. 2)
    3. 3)
      • 7. Proakis, J.G., Salehi, M.: ‘Digital communications’ (McGraw-Hill, New York, 2007, 5th edn.).
    4. 4)
      • 13. Bellamy, J.C.: ‘Digital telephony’ (John Wiley & Sons Inc., New York, 2000, 3rd edn.).
    5. 5)
      • 12. Chen, S., Zeng, K., Cheng, N., Mohapatra, P.: ‘Transmit power estimation with a single monitor in multi-band networks’. IEEE Commun. Society Conf. SECON, Seoul, June 2012, pp. 524532.
    6. 6)
    7. 7)
      • 11. Ho, I.W., Ko, B.J., Zafer, M.: ‘Blind estimation of transmit-power for multiple wireless sources’. Military Communications Conf., San Diego, CA, November 2008, pp. 17.
    8. 8)
      • 5. Kay, S.M.: ‘Fundamentals of statistical signal processing: estimation theory’ (Hamilton in Castleton, New York. Nineteenth printing, 2011).
    9. 9)
      • 17. Chen, Y., Beaulieu, N.C.: ‘Performance of collaborative spectrum sensing for cognitive radio in the presence of Gaussian channel estimation errors’, IEEE Trans. Commun., 2009, 57, pp. 19411944.
    10. 10)
    11. 11)
    12. 12)
      • 2. Mishra, S.M., Sahai, A., Brodersen, R.W.: ‘Cooperative sensing among cognitive radios’. Proc. IEEE Int. Conf. Communication, Istanbul, Turkey, 2006, pp. 16581663.
    13. 13)
    14. 14)
      • 10. Ho, I.W., Ko, B.J., Zafer, M., Bisdikian, C., Leung, K.K.: ‘Cooperative transmit-power estimation in MANETs’. WCNC, 2008.
    15. 15)
      • 1. Visotsky, E., Kuffner, S., Peterson, R.: ‘On collaborative detection of TV transmissions in support of dynamic spectrum sharing’. Proc. IEEE Symp. New Frontiers in Dynamic Spectrum Access Networks (DySPAN), Baltimore, MD, November 2005, pp. 338345.
    16. 16)
    17. 17)
      • 6. Chen, Y., Beaulieu, N.C.: ‘Estimation of Ricean and Nakagami distribution parameters using noisy samples’. Proc. IEEE ICC 2004, Paris, France, June 2004.
    18. 18)
      • 16. Papoulis, A.: ‘Probability, random variables, and stochastic processes’ (McGraw-Hill, 1991, 3rd edn.).
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