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User Identification Methods in Cognitive Radio Networks

  • A. K. Budati
  • S. Kiran Babu
  • Ch. Suneetha
  • B. B. Reddy
  • P. V. Rao
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 768)

Abstract

Spectrum sensing is the process of detection of the user whether it is present or absent. Spectrum Sensing is the key role in Cognitive Radio (CR) Networks and thus improving spectrum utilization. The major issues that may arise in spectrum sensing are Probability of false alarm (Pfa) and Probability of miss detection (Pmd). An analytical comparison is proposed between two of Non-Cooperative detection methods in the spectrum sensing. In this paper, an attempt is made to identify a better detection method based on high Probability Detection (PD).

Keywords

Probability of false alarm Probability of detection Probability of miss detection Cognitive radio Matched filter Cyclostationary feature detector 

Notes

Acknowledgements

The work has been carried out in DST-FIST lab (SR/FST/College-209/2014) provided at Vignana Bharathi Institute of Technology, Hyderabad, Telangana.

References

  1. 1.
    Akyildiz, I.F., Lee, W.Y.: A survey on spectrum management in cognitive radio networks. IEEE Commun. Mag. (2008)Google Scholar
  2. 2.
    Gorcin, A., Qaraqe, K.A.: An adaptive threshold method for spectrum sensing in multi-channel cognitive radio networks. In: IEEE, 17th international conference on telecommunications (2010)Google Scholar
  3. 3.
    Anil Kumar, B., Trinatha Rao, P.: Overview of advances in communication technologies. In: INCEMIC Conference Proceedings, pp. 47–51 (2015)Google Scholar
  4. 4.
    Tertinek, S.: Optimal detection of deterministic and random signals. Adv. Sig. Process. (2002)Google Scholar
  5. 5.
    Urkowitz, H.: Energy detection of unknown deterministic signals. Proc. IEEE 55(4) (1967)Google Scholar
  6. 6.
    Alvi, S.A.: A log-probability based cooperative spectrum sensing scheme for cognitive radio networks. ELSEVIER J. Emer. Ubiquitous Syst. Pervasive Networks, Procedia Comput. Sci. 3, 196–202 (2014)Google Scholar
  7. 7.
    Vadivelu, R.: MFDI-SS based spectrum sensing for cognitive radio at low signal to noise ratio. J. Theor. Appl. Inf. Technol. 62, 107–113 (2014)Google Scholar
  8. 8.
    Lee, Y.: Cyclostationary based detection of randomly arriving or departing signals. ELSEVIER J. Appl. Res. Technol. 12, 1083–1091 (2014)CrossRefGoogle Scholar
  9. 9.
    Scott, C.: A Neyman-pearson approach to statistical learning. IEEE Trans. Inf. Theory 51(11) (2005)Google Scholar
  10. 10.
    Yang, L.: Cyclo-energy detector for spectrum sensing in cognitive radio. ELSEVIER Article Lett. Int. J. Electron. Commun. 89–92 (2012)Google Scholar
  11. 11.
    Anil Kumar, B., Trinatha Rao, P.: CFDI-SS: Cyclostationary feature detector with inverse covariance matrix based spectrum sensing in Cognitive Radio. In: Smart tech conference proceedings (2017)Google Scholar
  12. 12.
    Skolink, M.I.: Introduction to radar principles. In: Tata McGraw hill third edition, pp. 284–285 (2008)Google Scholar
  13. 13.
    Cabric, D.: Implementation issues in spectrum sensing for cognitive radios white letter clayton scott a neyman pearson approach to statistical learning. IEEE Tran. Inf. Hypotheses 51(11), 3806–3819 (2005)Google Scholar
  14. 14.

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • A. K. Budati
    • 1
  • S. Kiran Babu
    • 1
  • Ch. Suneetha
    • 1
  • B. B. Reddy
    • 1
  • P. V. Rao
    • 1
  1. 1.Department of ECEVBITHyderabadIndia

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