Skip to main content
SpringerLink
Log in

Optimal Detection of Faded Pilot Signal in MIMO Channels with Applications in Cognitive Radio Systems

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Multiple transmit and receive antenna arrays can be used to form MIMO systems to improve spectrum sensing cycle of recently proposed cognitive radio (CRs) systems by utilizing diversity and/or multiplexing techniques. This paper designs an optimal spectrum detection for multiple antenna CR with a multiple antenna primary user as a backhand licensed network while the primary network protocol uses preamble or pilot signal. The MIMO channel between the primary and secondary users is modeled by Rayleigh distribution with arbitrary coherence time which is proper for slow to fast fading environments. In this situation, optimal detector is presented and closed-form expressions for probability of detection and false alarm are derived. Analytical and simulation results demonstrate that the performance of the proposed scheme outperforms other existing suboptimal detectors.

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

Similar content being viewed by others

References

  1. Spectrum policy task force report (et docket no. 02–135) (2002). Tech. Rep.

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

    Article  Google Scholar 

  3. Zeng, Y., Liang, Y.-C., Hoang, A. T., & Zhang, R. (2010). A review on spectrum sensing for cognitive radio: Challenges and solutions. In EURASIP Journal on Advances in Signal Processing, 2010, pp. 2.

  4. Tian, Z., & Giannakis, G. B. (2006). A wavelet approach to wideband spectrum sensing for cognitive radios. In Cognitive radio oriented wireless networks and communications, 2006. 1st international conference on, pp. 1–5.

  5. Liang, Y.-C., Zeng, Y., Peh, E., & Hoang, A. T. (2008). Sensing-throughput tradeoff for cognitive radio networks. IEEE Transactions on Wireless Communications, 7(4), 1326–1337.

    Article  Google Scholar 

  6. Cabric, D., Tkachenko, A., & Brodersen, R. (2006). Spectrum sensing measurements of pilot, energy, and collaborative detection. In Military communications conference, 2006. MILCOM 2006. IEEE, pp. 1–7.

  7. Ye, Z., Grosspietsch, J., & Memik, G. (2007). Spectrum sensing using cyclostationary spectrum density for cognitive radios. In Signal processing systems, 2007 IEEE workshop on, pp. 1–6.

  8. Mohammadkarimi, M., Mahboobi, B., & Ardebilipour, M. (2010). Cooperative proactive spectrum sensing for cognitive radio networks. In Wireless communications networking and mobile computing (WiCOM), 2010 6th international conference on, pp. 1–4.

  9. Mohammadkarimi, M., Mahboobi, B., & Ardebilipour, M. (2010). Glrt based active spectrum sensing for cognitive radio systems. In Wireless communications networking and mobile computing (WiCOM), 2010 6th international conference on. IEEE, pp. 1–4.

  10. Mohammadkarimi, M., Mahboobi, B., & Ardebilipour, M. (2014). Non-linear space-time kalman filter for cooperative spectrum sensing in cognitive radios. IET Communications, 8(1), 92–104.

    Article  Google Scholar 

  11. Mohammadkarimi, M., Mahboobi, B., & Ardebilipour, M. (2011). Optimal spectrum sensing in fast fading rayleigh channel for cognitive radio. Communications Letters, IEEE, 15(10), 1032–1034.

    Article  Google Scholar 

  12. Mahboobi, B., Mohammadkarimi, M., & Ardebilipour, M. (2014). Spatial-temporal cooperative spectrum sensing in flat fading channels for cognitive radio using extend kalman filter. Wireless Personal Communications, 75(1), 195–218.

    Article  Google Scholar 

  13. Kazemi, M., Mahboobi, B., & Ardebilipour, M. (2011). Performance analysis of simultaneous location and power estimation using WLS method for cognitive radio. Communications Letters, IEEE, 15(10), 1062–1064.

  14. Kazemi, M., Ardebilipour, M., & Mahboobi, B. (2012). Statistical analysis of linear spatial holes estimators in cognitive radio. EURASIP Journal on Wireless Communications and Networking, 2012(1), 1–5.

  15. Mietzner, J., Schober, R., Lampe, L., Gerstacker, W., & Hoeher, P. (2009). Multiple-antenna techniques for wireless communications: A comprehensive literature survey. Communications Surveys Tutorials, IEEE, 11(2), 87–105. quarter.

    Article  Google Scholar 

  16. Neihart, N., Roy, S., & Allstot, D. (2007). A parallel, multi-resolution sensing technique for multiple antenna cognitive radios. In Circuits and systems, 2007. ISCAS 2007. IEEE international symposium on, pp. 2530–2533.

  17. Kuppusamy, V., & Mahapatra, R. (2008). Primary user detection in ofdm based mimo cognitive radio. In Cognitive radio oriented wireless networks and communications, 2008. CrownCom 2008. 3rd international conference on, pp. 1–5.

  18. Pandharipande, A., & Linnartz, J.-P. (2007). Performance analysis of primary user detection in a multiple antenna cognitive radio. In Communications, 2007. ICC ’07. IEEE international conference on, pp. 6482–6486.

  19. Axell, E., Leus, G., Larsson, E., & Poor, H. (2012). Spectrum sensing for cognitive radio: State-of-the-art and recent advances. Signal Processing Magazine, IEEE, 29(3), 101–116.

    Article  Google Scholar 

  20. Axell, E., & Larsson, E. (2010). Spectrum sensing of orthogonal space-time block coded signals with multiple receive antennas. In Acoustics speech and signal processing (ICASSP). IEEE international conference on, pp. 3110–3113.

  21. Ye, Z., Grosspietsch, J., & Memik, G. (2007). Spectrum sensing using cyclostationary spectrum density for cognitive radios. In Signal processing systems. IEEE workshop on, pp. 1–6.

  22. Chaudhari, S., Koivunen, V., & Poor, H. (2009). Autocorrelation-based decentralized sequential detection of ofdm signals in cognitive radios. IEEE Transactions on Signal Processing, 57(7), 2690–2700.

    Article  MathSciNet  Google Scholar 

  23. Couillet, R., & Debbah, M. (2010). A bayesian framework for collaborative multi-source signal sensing. IEEE Transactions on Signal Processing, 58(10), 5186–5195.

    Article  MathSciNet  Google Scholar 

  24. Taherpour, A., Nasiri-Kenari, M., & Gazor, S. (2010). Multiple antenna spectrum sensing in cognitive radios. IEEE Transactions on Wireless Communications, 9(2), 814–823.

    Article  Google Scholar 

  25. Wang, P., Fang, J., Han, N., & Li, H. (2010). Multiantenna-assisted spectrum sensing for cognitive radio. IEEE Transactions on Vehicular Technology, 59(4), 1791–1800.

    Article  Google Scholar 

  26. Zeng, Y., & Liang, Y.-C. (2009). Eigenvalue-based spectrum sensing algorithms for cognitive radio. IEEE Transactions on Communications, 57(6), 1784–1793.

    Article  Google Scholar 

  27. Van Trees, H. (2004). Detection, estimation, and modulation theory, optimum array processing, ser. detection, estimation, and modulation theory. London: Wiley.

    MATH  Google Scholar 

  28. Proakis, J., & Salehi, M. (2008). Digital communications, ser. McGraw-Hill higher education. NY: McGraw-Hill.

  29. Kreyszig, E. (2010). Advanced engineering mathematics. London: Wiley.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Behrad Mahboobi

  2. Faculty of Electrical and Computer Engineering, K.N. Toosi University of Technology, Tehran, Iran

    Mehrdad Ardebilipour & Mostafa Mohammadkarimi

Authors
  1. Behrad Mahboobi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mehrdad Ardebilipour
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mostafa Mohammadkarimi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Behrad Mahboobi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mahboobi, B., Ardebilipour, M. & Mohammadkarimi, M. Optimal Detection of Faded Pilot Signal in MIMO Channels with Applications in Cognitive Radio Systems. Wireless Pers Commun 83, 1579–1593 (2015). https://doi.org/10.1007/s11277-015-2465-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-015-2465-4

Keywords

Search

Navigation