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International Journal of Information Technology

, Volume 11, Issue 4, pp 773–778 | Cite as

Sensing performance of energy detector in cognitive radio networks

  • Samit Kumar GhoshEmail author
  • Jishan Mehedi
  • Umesh Chandra Samal
Original Research
  • 17 Downloads

Abstract

In order to increase the spectral efficiency of any communication systems, spectrum sensing techniques may be used for proficient utilization of inadequate spectrum resources. It identifies the unused spectrum holes, which is originally assigned to the primary users (PU). These spectrum holes are then assigned to the secondary or cognitive users with avoiding interference to the primary users. In this paper, a spectrum assignment technique based on energy detection technique is proposed. This enhanced energy detection technique works well at low signal-to-noise ratio (SNR), which makes the communication system more power efficient and can be for low power applications. Further, the performance of the proposed spectrum sensing method is examined for cognitive radio (CR) network. The performance of the proposed method is also examined by calculating the probability of detection, probability of false alarm and error probability in presence of additive Gaussian noise and the effect of different sensing parameters on the probability of error in detecting primary users are also evaluated.

Keywords

Cognitive radio networks Energy detection Spectrum sensing Probability of detection 

References

  1. 1.
    Akyildiz MVIF, Lee WY, Mohanty S (2006) Next generation/dynamic spectrum access/cognitive radio wireless networks: a survey. Comput Netw J (Elsevier) 50:2127–2159CrossRefGoogle Scholar
  2. 2.
    Urkowitz Harry (1967) Energy detection of unknown deterministic signals. Proc IEEE 55(4):523–531CrossRefGoogle Scholar
  3. 3.
    Cabric D, Tkachenko A, Brodersen RW (2006) Spectrum sensing measurements of pilot, energy, and collaborative detection. IEEE Mil Commun Conf (MILCOM) 2006:1–7Google Scholar
  4. 4.
    Chen J, Gibson A, Zafar J (2008) Cyclostationary spectrum detection in cognitive radios. IET Seminar on Cognitive radio and software defined radios: technologies and techniques, 2008 pp 1–5Google Scholar
  5. 5.
    Zeng Y, Liang YC (2009) Spectrum-sensing algorithms for cognitive radio based on statistical covariances. IEEE Trans Veh Technol 58(4):1804–1815CrossRefGoogle Scholar
  6. 6.
    Ariananda DD, Lakshmanan MK, Nikookar H (2009) A survey on spectrum sensing techniques for cognitive radio. Second International Workshop on Cognitive radio and advanced spectrum management, 2009. CogART 2009. pp 74–79Google Scholar
  7. 7.
    Proakis JG (2011) Digital communications, 4th edn. McGraw-Hill, New YorkzbMATHGoogle Scholar
  8. 8.
    Urkowitz Harry (1967) Energy detection of unknown deterministic signals. Proc IEEE 55(4):523–531CrossRefGoogle Scholar
  9. 9.
    Digham FF, Alouini MS, Simon MK (2003) On the energy detection of unknown signals over fading channels. IEEE International Conference on Communications, 2003. ICC’03. vol. 5, pp. 3575–3579Google Scholar
  10. 10.
    Digham FF, Alouini MS, Simon MK (2007) On the energy detection of unknown signals over fading channels. IEEE Trans Commun 55(1):21–24CrossRefGoogle Scholar
  11. 11.
    Kostylev VI (2002) Energy detection of a signal with random amplitude. IEEE International Conference on Communications, 2002. ICC 2002 3:1606–1610Google Scholar
  12. 12.
    Tandra R, Sahai A (2008) SNR walls for signal detection. IEEE J Sel Top Signal Process 2(1):4–17CrossRefGoogle Scholar
  13. 13.
    Natasha S, Nitin P, Ajeet PS (2018) Security enhancement technique in cognitive networks. Int J Inf Technol.  https://doi.org/10.1007/s41870-018-0183-3 CrossRefGoogle Scholar
  14. 14.
    Khalaf Z, Nafkha A, Palicot J (2011) Enhanced hybrid spectrum sensing architecture for cognitive radio equipment. General Assembly and Scientific Symposium, 2011 XXXth URSI, pp 1–4Google Scholar
  15. 15.
    Moghimi F, Schober R, Mallik RK (2011) Hybrid coherent/energy detection for cognitive radio networks. IEEE Trans Wireless Commun 10(5):1594–1605CrossRefGoogle Scholar
  16. 16.
    Ghosh SK, Bachan P (2017) Performance evaluation of spectrum sensing techniques in cognitive radio network. IOSR J Electron Commun Eng (IOSR-JECE) e-ISSN: 2278–2834, p-ISSN: 2278–8735. 12(4):2Google Scholar
  17. 17.
    Haykin S, Thomson DJ, Reed JH (2009) Spectrum sensing for cognitive radio. Proc IEEE 97(5):849–877CrossRefGoogle Scholar
  18. 18.
    Perera L, Herath H (2011) Review of spectrum sensing in cognitive radio. In: industrial and information systems (ICIIS), 2011 6th IEEE International Conference on. IEEE, 2011, pp. 7–12Google Scholar
  19. 19.
    Dalai J, Patra SK (2013) Spectrum sensing for wlan and WiMAX using energy detection technique. In: Emerging Trends in Computing, Communication and Nanotechnology (ICE-CCN), 2013 International Conference on. IEEE, 2013, pp. 620–624Google Scholar
  20. 20.
    Bachan P, Ghosh SK, Saraswat SK (2015) Comparative error rate analysis of cooperative spectrum sensing in non-fading and fading environment. IEEE Int Conf Commun Control Intell Syst (CCIS 2015) Pp 124–127, ISBN: 978-1-4673-7540-5,  https://doi.org/10.1109/ccintels.2015.7437891
  21. 21.
    Sharma A, Chauhan A (2016) Spectrum sensing based on multiple energy detector for cognitive radio systems under noise uncertainty. IEEE 1st International Conference on power electronics, intelligent control and energy systems (ICPEICES), Pp 1–4, ISBN: 978-1-4673-8587-9,  https://doi.org/10.1109/icpeices.2016.7853328

Copyright information

© Bharati Vidyapeeth's Institute of Computer Applications and Management 2018

Authors and Affiliations

  • Samit Kumar Ghosh
    • 1
    Email author
  • Jishan Mehedi
    • 2
  • Umesh Chandra Samal
    • 3
  1. 1.Department of Electronics and Communication EngineeringMLR Institute of TechnologyHyderabadIndia
  2. 2.Department of Electronics and Communication EngineeringJalpaiguri Government Engineering CollegeJalpaiguriIndia
  3. 3.School of Electronics EngineeringKIIT UniversityBhubaneswarIndia

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