Skip to main content
SpringerLink
Log in

Improving Sensing and Throughput of the Cognitive Radio Network

  • Published:
Circuits, Systems, and Signal Processing Aims and scope Submit manuscript

Abstract

In a cognitive radio network, when primary user’s (PU’s) spectrum is periodically sensed, secondary user (SU) needs to wait during sensing interval, which causes interruption in SUs’ transmission and significant reduction in achievable throughput. If continuous spectrum sensing is implemented, then a portion of a band is always unavailable for SUs’ transmission, which also limits the throughput. In this paper, we propose two methods. With the first one, we achieve an increase in throughput, while SUs’ transmission is uninterrupted, and in the second method, we let our algorithm dynamically choose time-band portion of a frame for spectrum sensing, causing minimal interference to PU than the one in first method and offering uninterrupted service to cognitive radio (CR) users. Simulation results show that achievable throughput is improved significantly in both methods. It also showed that the methods offer normalized throughput between 4.7 and 5.3 bits/s/Hz over a wider range of sensing band from 0.75 to 6 MHz, when PU’s active phase probability is 0.2, whereas delay-oriented continuous spectrum sensing (DOCSS) scheme achieves this range within a narrower sensing band—especially at the optimal value. Results also showed that for an arbitrary sensing band, normalized throughput achieved by our methods is larger than the one achieved by DOCSS scheme.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Q. Chen, M. Motani, W.C. Wong, Y.C. Liang, Opportunistic spectrum access protocol for cognitive radio networks. In: IEEE international conference on communications (ICC), pp. 1–6 (2011). doi:10.1109/icc.2011.5962450.

  2. Y.J. Choi, W. Pak, Y. Xin, S. Rangarajan, Throughput analysis of cooperative spectrum sensing in Rayleigh-faded cognitive radio systems. IET Commun. 6(9), 1104–1110 (2012)

    Article  MathSciNet  Google Scholar 

  3. N. Duong, S. Tio, A. Madhukumar, A cooperative spectrum sensing technique with dynamic frequency boundary detection and information-entropy-fusion for primary user detection. Circuits Systems Signal Process. 30(4), 823–845 (2011)

    Article  MathSciNet  Google Scholar 

  4. S. Haykin, Cognitive radio: brain-empowered wireless communications. IEEE J. Sel. Areas Commun. 23(2), 201–220 (2005)

    Article  Google Scholar 

  5. A.T. Hoang, Y.C. Liang, Y. Zeng, Adaptive joint scheduling of spectrum sensing and data transmission in cognitive radio networks. IEEE Trans. Commun. 58(1), 235–246 (2010). doi:10.1109/TCOMM.2010.01.070270

    Article  Google Scholar 

  6. S.S. Hwang, D.C. Park, S.C. Kim, Frequency domain dtv pilot detection based on the Bussgang theorem for cognitive radio. ETRI J. 35(4), 644–654 (2013)

    Article  MathSciNet  Google Scholar 

  7. IEEE 802.22 Working Group, IEEE 802 LAN/MAN standards on wireless regional area networks. Tech. Rep. 55(4), (1967). http://www.ieee802.org/22

  8. W.Y. Lee, I. Akyildiz, Optimal spectrum sensing framework for cognitive radio networks. IEEE Trans. Wirel. Commun. 7(10), 3845–3857 (2008). doi:10.1109/T-WC.2008.070391

    Article  Google Scholar 

  9. W.Y. Lee, I. Akyldiz, A spectrum decision framework for cognitive radio networks. IEEE Trans. Mobile Comput. 10(2), 161–174 (2011). doi:10.1109/TMC.2010.147

    Article  Google Scholar 

  10. Y.C. Liang, Y. Zeng, E. Peh, A.T. Hoang, Sensing-throughput tradeoff for cognitive radio networks. IEEE Trans. Wirel. Commun. 7(4), 1326–1337 (2008)

    Article  Google Scholar 

  11. J. Ma, Y. Li, Soft combination and detection for cooperative spectrum sensing in cognitive radio networks. In: Global telecommunications conference, 2007. IEEE, GLOBECOM ’07. pp. 3139–3143 (2007). doi:10.1109/GLOCOM.2007.594

  12. J. Ma, G. Zhao, Y. Li, Soft combination and detection for cooperative spectrum sensing in cognitive radio networks. IEEE Trans. Wirel. Commun. 7(11), 4502–4507 (2008). doi:10.1109/T-WC.2008.070941

    Article  Google Scholar 

  13. J. Mitola, G.Q. Maguire Jr, Cognitive radio: making software radios more personal. IEEE Pers. Commun. 6(4), 13–18 (1999)

  14. A. Papoulis, P. Unnikrishna, Probability, random variables, and Stochastic processes, fourth edn (TATA McGraw-Hill, New York, 2006)

    Google Scholar 

  15. Spectrum policy task force, Report of the spectrum efficiency working group Tech. Rep., Federal Communications Commission, (2002)

  16. S. Stotas, A. Nallanathan, Overcoming the sensing-throughput tradeoff in cognitive radio networks. In: 2010 IEEE international conference on communications (ICC), pp. 1–5 (2010). doi:10.1109/ICC.2010.5502792

  17. H. Urkowitz, Energy detection of unknown deterministic signals. Proc. IEEE 55(4), 523–531 (1967)

    Article  Google Scholar 

  18. J. Wang, J. Huang, M. Fan, H. Wang, Nonparametric multicycle spectrum sensing method by segmented data processing for cognitive radio. Circuits. Syst. Signal Process. 33(1), 299–307 (2014)

  19. W. Yin, P. Ren, Q. Du, Y. Wang, Delay and throughput oriented continuous spectrum sensing schemes in cognitive radio networks. IEEE Trans. Wirel. Commun. 11(6), 2148–2159 (2012)

    Article  Google Scholar 

  20. Y. Zeng, Y.C. Liang, Spectrum-sensing algorithms for cognitive radio based on statistical covariances. IEEE Trans. Veh. Technol. 58(4), 1804–1815 (2009)

    Article  Google Scholar 

  21. W. Zhang, R. Mallik, K. Letaief, Optimization of cooperative spectrum sensing with energy detection in cognitive radio networks. IEEE Trans. Wirel. Commun. 8(12), 5761–5766 (2009). doi:10.1109/TWC.2009.12.081710

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Electronics Engineering, VIT University, Vellore, India

    J. Christopher Clement & D. S. Emmanuel

  2. Department of Electronics and Communication Engineering, Karunya University, Coimbatore, India

    J. Jenkin Winston

Authors
  1. J. Christopher Clement
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. D. S. Emmanuel
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. J. Jenkin Winston
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to J. Christopher Clement.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Christopher Clement, J., Emmanuel, D.S. & Jenkin Winston, J. Improving Sensing and Throughput of the Cognitive Radio Network. Circuits Syst Signal Process 34, 249–267 (2015). https://doi.org/10.1007/s00034-014-9845-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00034-014-9845-y

Keywords

Search

Navigation