Skip to main content

On Results’ Reporting of Cooperative Spectrum Sensing in Cognitive Radio Networks

Telecommunication Systems volume 62, pages 569–580 (2016)Cite this article

Abstract

One of the main challenges of cooperative spectrum sensing in cognitive radio networks is the overhead in terms of high energy consumption especially when reporting the individual sensing results to a common receiver. Such issue becomes more challenging for battery-powered users, because of its direct influence on achievable performance represented by detection accuracy. Thus, energy efficiency in cognitive radio networks has received a lot of attention during recent years. In this paper, we present a novel reporting scheme for spectrum sensing results, which significantly reduces the energy consumption without any effect on the detection accuracy. The proposed scheme allows the fusion center to terminate the reporting process whenever the received results are enough to make a decision according to the employed fusion rule (FR). Hence, the energy consumed in results’ reporting is reduced as the number of reporting users is lower, and the data transmission can be started earlier, which enhances the achievable throughput. Moreover, the proposed scheme is consistent with many other energy-efficient approaches, leading to improve the energy efficiency achieved by these approaches. Mathematical expressions for the average number of reporting users for several FRs are obtained. Simulation and analytical results show a significant improvement in the energy efficiency.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

References

  1. Fettweis, G., & Zimmermann, E. (2008). ICT energy consumption—trends and challenges. In WPMC Conference, Lapland.

  2. Goldsmith, A., Jafar, S. A., Maric, I., & Srinivasa, S. (2009). Breaking spectrum gridlock with cognitive radios: An information theoretic perspective. Proceedings of the IEEE, 97(5), 894–914.

    Article  Google Scholar 

  3. Srinivasa, S., & Jafar, S. A. (2007). Cognitive radios for dynamic spectrum accessthe throughput potential of cognitive radio: A theoretical perspective. IEEE Communications Magazine, 45(5), 73–79.

    Article  Google Scholar 

  4. Mesodiakaki, A., Adelantado, F., Alonso, L., & Verikoukis, C. (2015). Performance analysis of a cognitive radio contention-aware channel selection algorithm. IEEE Transactions on Vehicular Technology, 64(5), 1958–1972.

    Article  Google Scholar 

  5. Mitola, J., & Maguire, G. Q. (1999). Cognitive radio: Making software radios more personal. IEEE Personal Communications, 6(4), 13–18.

    Article  Google Scholar 

  6. Hossain, E., Niyato, D., & Kim, D. I. (2013). Evolution and future trends of research in cognitive radio: A contemporary survey. In Wireless Communications and Mobile Computing.

  7. Mishra, S. M., Sahai, A., & Brodersen, R. (2006). Cooperative sensing among cognitive radios. In IEEE ICC, Istanbul.

  8. Ghasemi, A., & Sousa, E. S. (2007). Opportunistic spectrum access in fading channels through collaborative sensing. Journal of Communications, 2(2), 71–82.

    Article  Google Scholar 

  9. Adelantado, F., Juan, A., & Verikoukis, C. (2010). Adaptive sensing user selection mechanism in cognitive wireless networks. IEEE Communications Letters, 14(9), 800–802.

    Article  Google Scholar 

  10. Cheng, P., Deng, R., & Chen, J. (2012). Energy-efficient cooperative spectrum sensing in sensor-aided cognitive radio networks. IEEE Wireless Communications, 19(6), 100–105.

    Article  Google Scholar 

  11. Chaudhari, S., Lunden, J., Koivunen, V., & Poor, H. V. (2012). Cooperative sensing with imperfect reporting channels: Hard decisions or soft decisions? In IEEE Transactions on Signal Processing, (Vol. 60, No. 1).

  12. Viswanathan, R., & Varshney, P. K. (1997). Distributed detection with multiple sensors. Proceedings of the IEEE, 85(1), 54–63.

    Article  Google Scholar 

  13. Akyildiz, I. F., Lo, B. F., & Balakrishnan, R. (2011). Cooperative spectrum sensing in cognitive radio networks: A survey. Physical Communication (Elsevier), 4(1), 40–62.

    Article  Google Scholar 

  14. Althunibat, S., Di Renzo, M., & Granelli, F. (2015). Towards energy-efficient cooperative spectrum sensing for cognitive radio networks: An overview. Telecommunication Systems, 59(1), 77–91.

    Article  Google Scholar 

  15. Maleki, S., Chepuri, S. P., & Leus, G. (2011). Energy and throughput efficient strategies for cooperative spectrum sensing in cognitive radios. In IEEE 12th International Workshop on Signal Processing Advances in Wireless Communications, San Francisco.

  16. Deng, R., Chen, J., Yuen, C., Cheng, P., & Sun, Y. (2012). Energy-efficient cooperative spectrum sensing by optimal scheduling in sensor-aided cognitive radio networks. In IEEE Transactions on Vehicular Technology, (Vol. 61, No. 2).

  17. Althunibat, S., Narayanan, S., Di Renzo, M., & Granelli, F. (2013). Energy-efficient partial-cooperative spectrum sensing in cognitive radio over fading channels. In IEEE VTC-Spring, Dresden.

  18. Pham, H. N., Zhang, Y., Engelstad, P. E., Skeie, T., & Eliassen, F. (2010). Energy minimization approach for optimal cooperative spectrum sensing in sensor-aided cognitive radio networks. In ICST-WICON, Singapore, (pp. 1–9).

  19. Althunibat, S., Di Renzo, M., & Granelli, F. (2013). Optimizing the K-out-of-N rule for cooperative spectrum sensing in cognitive radio networks. IEEE Global Communications Conference, Atlanta.

  20. Peh, E. C. Y., Liang, Y. C., Guan, Y. L., & Pei, Y. (2011). Energy-efficient cooperative spectrum sensing in cognitive radio networks. In IEEE Global Communications Conference, Houston.

  21. Althunibat, S., Di Renzo, M., & Granelli, F. (2014). Cooperative spectrum sensing for cognitive radio networks under limited time constraints. Computer Communications, 43, 55–63.

    Article  Google Scholar 

  22. Lee, C., & Wolf, W. (2008). Energy efficient techniques for cooperative spectrum sensing in cognitive radios. In IEEE Consumer Communications and Networking Conference CCNC, Las Vegas.

  23. Xia, W., Wang, S., Liu, W., & Cheng, W. (2009). Cluster-based energy efficient cooperative spectrum sensing in cognitive radios. In IEEE 5th International Conference on Wireless Communications, Networking and Mobile Computing (WiCom’09), Beijing.

  24. Zhang, W., & Yeo, C. K. (2012). Cluster-based adaptive multispectrum sensing and access in cognitive radio networks. In Wireless Communications and Mobile Computing.

  25. Maleki, S., Pandharipande, A., & Leus, G. (2009). Energy-efficient spectrum sensing for cognitive sensor networks. In IEEE 35th Annual Conference of Industrial Electronics IECON, Porto Portugal.

  26. Alvi, S. A., Younis, M. S., & Imran, M. (2014). A weighted linear combining scheme for cooperative spectrum sensing. Procedia Computer Science, 32, 149–157.

    Article  Google Scholar 

  27. Alvi, S. A., Younis, M. S., Imran, M., Amin, F., & Guizani, M. (2015). A near-optimal LLR based cooperative spectrum sensing scheme for CRAHNs. IEEE Transactions on Wireless Communications, 14(7), 3877–3887.

    Article  Google Scholar 

  28. Althunibat, S., & Granelli, F. (2013). Novel energy-efficient reporting scheme for spectrum sensing results in cognitive radio. In IEEE International Conference on Communications (ICC), Budapest.

  29. Hsu, M. F., Wang, T. Y., & Yu, C. T. (2013). A unified spectrum sensing and throughput analysis model in cognitive radio networks. In Wireless Communications and Mobile Computing.

  30. Digham, F., Alouini, M-S., & Simon, M. K. (2003). “On the energy detection of unknown signals over fading channels. IEEE International Conference on Communications ICC, Anchorage.

  31. Gradshteyn, I. S., & Ryzhik, I. M. (2000). Table of Integrals, Series, and Products (6th ed.). San Diego: Academic Press.

    Google Scholar 

  32. Chen, B., Jiang, R., Kasetkasem, T., & Varshney, P. K. (2004). Channel aware decision fusion in wireless sensor networks. IEEE Transactions on Signal Processing, 52(12), 3454–3458.

    Article  Google Scholar 

  33. Nuttall, A. H. (1975). Some integrals involving the \(Q_M\) function. IEEE Transactions on Information Theory, 21(1), 9596.

    Article  Google Scholar 

  34. Varshney, P. K. (1997). Distributed detection and data fusion. New York: Springer.

    Book  Google Scholar 

  35. Wang, Y., Feng, C., Zeng, Z., & Guo, C. (2009). A robust and energy efficient cooperative spectrum sensing scheme in cognitive radio networks. In IEEE ICACT, Gangwon-Do.

  36. Sun, Ch., Zhang, W., & Ben, K. (2007). Cluster-based cooperative spectrum sensing in cognitive radio systems. In IEEE ICC, Glasgow.

Download references

Acknowledgments

This work is funded by the Research Project GREENET (PITN-GA-2010-264759).

Author information

Authors and Affiliations

  1. Department of Communication Engineering, Al-Hussein Bin Talal University, Ma’an, Jordan

    Saud Althunibat

  2. DISI, University of Trento, Via Sommarive 14, 38123, Trento, Italy

    Fabrizio Granelli

Authors
  1. Saud Althunibat
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fabrizio Granelli
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Saud Althunibat.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Althunibat, S., Granelli, F. On Results’ Reporting of Cooperative Spectrum Sensing in Cognitive Radio Networks. Telecommun Syst 62, 569–580 (2016). https://doi.org/10.1007/s11235-015-0095-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11235-015-0095-5

Keywords

  • Cognitive radio networks
  • Specturm sensing
  • Cooperative specturm sensing
  • Energy efficiency