Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Adaptive Counting Rule for Cooperative Spectrum Sensing Under Correlated Environments

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Spectrum sensing is the cognitive radio mechanism that enables spectrum awareness. It has been shown in the literature that spectrum sensing performance can be greatly improved through the use of cooperative sensing schemes. This paper considers and proposes a data fusion based cooperative spectrum sensing scheme based on data fusion, where an adaptive counting rule is used to implement the data fusion. The proposed scheme is evaluated against other common counting rules (e.g., 1-out-of-c and c-out-of-c) found in the literature and the optimum counting rule, while under different correlation conditions. The impact of correlation on the performance of the considered counting rules is then studied. It is concluded that the proposed adaptive counting rule detection performance reaches in some cases the one of the optimum counting rule, and therefore it adapts to the correlation conditions which the network nodes are experiencing.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Akyildiz I. F., Lo B. F., Balakrishnan R. (2011) Cooperative spectrum sensing in cognitive radio networks: A survey. Physical Communication 4(1): 40–62. doi:10.1016/j.phycom.2010.12.003

    Article  Google Scholar 

  2. Cabric, D., Tkachenko, A., & Brodersen, R.W. (2006). Experimental study of spectrum sensing based on energy detection and network cooperation. In Proceedings of the First International Workshop on Technology and Policy for Accessing Spectrum, TAPAS ’06. ACM, New York, NY, USA. doi:10.1145/1234388.1234400.

  3. Chen J. G., Ansari N. (1998) Adaptive fusion of correlated local decisions. IEEE Transactions on Systems, Man, and Cybernetics 28(2): 276–281. doi:10.1109/5326.669570

    Google Scholar 

  4. Drakopoulos E., Lee C. C. (1991) Optimum multisensor fusion of correlated local decisions. IEEE Transactions on Aerospace Electronic Systems 27: 593–606. doi:10.1109/7.85032

    Article  Google Scholar 

  5. Ghasemi, A., & Sousa, E. (2005). Collaborative spectrum sensing for opportunistic access in fading environments. In 2005 First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005 (pp. 131–136). doi:10.1109/DYSPAN.2005.1542627.

  6. Ghasemi A., Sousa E. (2007) Asymptotic performance of collaborative spectrum sensing under correlated log-normal shadowing. IEEE Communications Letters 11(1): 34–36. doi:10.1109/LCOMM.2007.060662

    Article  Google Scholar 

  7. Kim H., Shin K. (2008) Efficient discovery of spectrum opportunities with mac-layer sensing in cognitive radio networks. IEEE Transactions on Mobile Computing 7(5): 533–545. doi:10.1109/TMC.2007.70751

    Article  MathSciNet  Google Scholar 

  8. Ma J., Li G., Juang B. H. (2009) Signal processing in cognitive radio. Proceedings of the IEEE 97(5): 805–823. doi:10.1109/JPROC.2009.2015707

    Article  Google Scholar 

  9. Mishra, S., Sahai, A., & Brodersen, R. (2006). Cooperative sensing among cognitive radios. In IEEE International Conference on Communications, 2006. ICC ’06 (Vol. 4, pp. 1658–1663). doi:10.1109/ICC.2006.254957.

  10. Mitola J., Maguire G. (1999) Cognitive radio: Making software radios more personal. IEEE Personal Communications 6(4): 13–18. doi:10.1109/98.788210

    Article  Google Scholar 

  11. Mood A. M. (1974) Introduction to the theory of statistics, (3rd ed.). McGraw-Hill series in probability and statistics. McGraw-Hill, New York, NY

    Google Scholar 

  12. Pratas, N., Marchetti, N., Prasad, N., Prasad, R., & Rodrigues, A. (2010). Adaptive counting rule for cooperative spectrum sensIng under correlated environments. In Proceedings of Wireless Personal Multimedia Communications Symposia 2010, (pp. S11–1).

  13. Pratas N., Marchetti N., Prasad N., Prasad R., Rodrigues A. (2010) System capacity limits introduced by data fusion on cooperative spectrum sensing under correlated environments. MTA Review XX(4): 245–262

    Google Scholar 

  14. Pratas, N., Marchetti, N., Prasad, N., Rodrigues, A., & Prasad, R. (2010). Centralized cooperative spectrum sensing for ad-hoc disaster relief network clusters. In IEEE International Conference on Communications (ICC), 2010 (pp. 1–5). doi:10.1109/ICC.2010.5502710.

  15. Pratas, N., Marchetti, N., Prasad, N. R., Rodrigues, A., & Prasad, R. (2010). Decentralized cooperative spectrum sensing for ad-hoc disaster relief network clusters. In 2010 IEEE 71st on Vehicular Technology Conference (VTC 2010-Spring) (pp. 1–5). doi:10.1109/VETECS.2010.5494012.

  16. Urkowitz H. (1967) Energy detection of unknown deterministic signals. Proceedings of the IEEE 55(4): 523–531. doi:10.1109/PROC.1967.5573

    Article  Google Scholar 

  17. Varshney P. K. (1996) Distributed detection and data fusion. Springer, New York, NY, Secaucus, NJ

    Google Scholar 

  18. Vergara L. (2007) On the equivalence between likelihood ratio tests and counting rules in distributed detection with correlated sensors. Signal Processing 87(7): 1808–1815. doi:10.1016/j.sigpro.2007.01.023

    Article  MATH  Google Scholar 

  19. Visotsky, E., Kuffner, S., & Peterson, R. (2005). On collaborative detection of tv transmissions in support of dynamic spectrum sharing. In 2005 First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005 (pp. 338–345). doi:10.1109/DYSPAN.2005.1542650.

Download references

Author information

Authors and Affiliations

  1. Center for TeleInFrastruktur (CTIF), Aalborg University (AAU), Niels Jernes Vej 12, 9220, Aalborg Øst, Denmark

    Nuno Pratas, Nicola Marchetti, Neeli Rashmi Prasad & Ramjee Prasad

  2. Instituto Superior Técnico (IST/UTL), Instituto de Telecomunicações (IT), Av. Rovisco Pais, 1, 1049-001, Lisbon, Portugal

    Nuno Pratas & António Rodrigues

Authors
  1. Nuno Pratas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nicola Marchetti
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Neeli Rashmi Prasad
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. António Rodrigues
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ramjee Prasad
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nuno Pratas.

Additional information

The authors would like to acknowledge the FCT which supported this work under the grant SFRH/BD/36454/2007 and IT/LA.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Pratas, N., Marchetti, N., Prasad, N.R. et al. Adaptive Counting Rule for Cooperative Spectrum Sensing Under Correlated Environments. Wireless Pers Commun 64, 93–106 (2012). https://doi.org/10.1007/s11277-012-0519-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-012-0519-4

Keywords

Search

Navigation