Channel Availability Assessment for Cognitive Radios

  • António Furtado
  • Miguel Luís
  • Rodolfo Oliveira
  • Rui Dinis
  • Luis Bernardo
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 394)

Abstract

This work addresses two problems related with the assessment of channel availability for cognitive radio systems. We start to characterize the performance of an energy detector for the case when PUs can change their state during the sensing period. The theoretical performance is validated through simulations and compared with a theoretical model where the PUs’ state remains constant during the sensing period. The second point addressed in this work is the characterization of the channel availability, which is based on the output of the energy detector weighted by the probabilities of detection or false alarm computed in real-time. Several scenarios were evaluated and for each scenario the channel availability was correctly assessed by the SUs.

Keywords

Cognitive radio networks Energy Sensing System Analysis 

References

  1. 1.
    Yucek, T., Arslan, H.: A survey of spectrum sensing algorithms for cognitive radio applications. IEEE Communications Surveys Tutorials 11, 116–130 (2009)CrossRefGoogle Scholar
  2. 2.
    Zahedi-Ghasabeh, Tarighat, A., Daneshrad, B.: Spectrum Sensing of OFDM Waveforms Using Embedded Pilots in the Presence of Impairments. IEEE Transactions on Vehicular Technology 61, 1208–1221 (2012)CrossRefGoogle Scholar
  3. 3.
    Bouzegzi, A., Ciblat, P., Jallon, P.: Matched Filter Based Algorithm for Blind Recognition of OFDM Systems. In: Proc. IEEE VTC 2008-Fall, pp. 1–5 (September 2008)Google Scholar
  4. 4.
    Al-Habashna, A., Dobre, O., Venkatesan, R., Popescu, D.: Cyclostationarity-Based Detection of LTE OFDM Signals for Cognitive Radio Systems. In: Proc. IEEE GLOBECOM 2010, pp. 1–6 (December 2010)Google Scholar
  5. 5.
    Urkowitz, H.: Energy Detection of Unknown Deterministic Signals. Proceedings of the IEEE 55, 523–531 (1967)CrossRefGoogle Scholar
  6. 6.
    Ghasemi, A., Sousa, E.S.: Optimization of Spectrum Sensing for Opportunistic Spectrum Access in Cognitive Radio Networks. In: Proc. IEEE CCNC 2007, pp. 1022–1026 (January 2007)Google Scholar
  7. 7.
    Bhargavi, D., Murthy, C.: Performance comparison of energy, matched-filter and cyclostationarity-based spectrum sensing. In: Proc. IEEE SPAWC 2010, pp. 1–5 (2010)Google Scholar
  8. 8.
    Tian, Z., Giannakis, G.: Compressed Sensing for Wideband Cognitive Radios. In: IEEE ICASSP 2007, vol. 4, pp. IV-1357–IV-1360 (April 2007)Google Scholar
  9. 9.
    Lee, W.-Y., Akyildiz, I.F.: Optimal spectrum sensing framework for cognitive radio networks. IEEE Transactions on Wireless Communications 7(10), 3845–3857 (2008)CrossRefGoogle Scholar
  10. 10.
    Liang, Y.-C., Zeng, Y., Peh, E.C.Y., Hoang, A.T.: Sensing-Throughput Tradeoff or Cognitive Radio Networks. IEEE Transactions on Wireless Communications 7(4), 1326–1337 (2008)CrossRefGoogle Scholar
  11. 11.
    Wang, S., Zhang, J., Tong, L.: A characterization of delay performance of cognitive medium access. IEEE Transactions on Wireless Communications 11(2), 800–809 (2012)CrossRefGoogle Scholar
  12. 12.
    Tang, H.: Some physical layer issues of wide-band cognitive radio systems. In: Proc. IEEE DySPAN 2005, pp. 151–159 (November 2005)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2013

Authors and Affiliations

  • António Furtado
    • 2
    • 3
  • Miguel Luís
    • 1
    • 2
  • Rodolfo Oliveira
    • 1
  • Rui Dinis
    • 1
    • 2
  • Luis Bernardo
    • 1
  1. 1.CTS, Uninova, Dep.° de Eng.a Electrotécnica, Faculdade de Ciências e Tecnologia, FCTUniversidade Nova de LisboaCaparicaPortugal
  2. 2.IT, Instituto de TelecomunicaçõesPortugal
  3. 3.ISR, Instituto de Sistemas e RobóticaPortugal

Personalised recommendations