Skip to main content
Log in

Density Function of the Received Signal in OFDM-based Distributed Detection Fusion System

  • Published:
International Journal of Wireless Information Networks Aims and scope Submit manuscript

Abstract

Probability density function (pdf) of the received signal is necessary for the distributed detection fusion when the unreliable channels between the local sensors and the fusion center are considered. Hitherto study has never been made on the statistical distribution of the demodulated OFDM signal over multipath fading channel. Modeling the OFDM system over the multi-path channel, we derived the pdf of the channel frequency domain impulse response by introducing the characteristic function. And the pdf of the demodulated OFDM signal on the receiver side is deduced based on the theory of spherically symmetric random vectors. In addition, an example of OFDM-based distributed detection fusion is presented to illustrate the application of the derived pdf. Simulations show that the fusion with signal statistic information generally does better than fusion without any signal statistic information, and achieves the same performance as the fusion with perfect channel state information if the channel signal-to-noise ratio is above 7 dB.

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

Access this article

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

Similar content being viewed by others

References

  1. B. Chen, L. Tong, and P. Varshney, “Channel-aware distributed detection in wireless sensor networks,” IEEE Signal Processing Magazine, vol. 23, no. 4, pp. 16–26, Jul. 2006.

    Article  MATH  Google Scholar 

  2. H. R. Ahmadi and A. Vosoughi, “Impact of wireless channel uncertainty upon distributed detection system,” IEEE Transactions on Wireless Communications, vol. 12, no. 6, pp. 2566–2577, Jun. 2013.

    Article  Google Scholar 

  3. Y. Yilmaz, G. V. Moustakides, and X. Wang, “Channel-aware decentralized detection via level-triggered sampling,” IEEE Transactions on Signal Processing, vol. 61, no. 2, pp. 300–315, Jan. 2013.

    Article  MathSciNet  Google Scholar 

  4. D. Ciuonzo, P. S. Rossi, and S. Dey, “Massive mimo channel-aware decision fusion,” IEEE Transactions on Signal Processing, vol. 63, no. 3, pp. 604–619, Feb. 2015.

    Article  MathSciNet  Google Scholar 

  5. B. Chen, R. Jiang, T. Kasetkasem, and P. Varshney, “Channel aware decision fusion in wireless sensor networks,” IEEE Transactions on Signal Processing, vol. 52, no. 12, pp. 3454–3458, 2004.

    Article  MathSciNet  Google Scholar 

  6. H. Mahmoud, T. Yucek, and H. Arslan, “OFDM for cognitive radio: merits and challenges,” IEEE Wireless Communications, vol. 16, no. 2, pp. 6–14, 2009.

    Article  Google Scholar 

  7. C. R. Berger, B. Demissie, J. Heckenbach, P. Willett, and S. Zhou, “Signal processing for passive radar using OFDM waveforms,” IEEE Journal of Selected Topics in Signal Processing, vol. 4, no. 1, pp. 226–238, Feb. 2010.

    Article  Google Scholar 

  8. S. Sen and A. Nehorai, “Adaptive OFDM radar for target detection in multipath scenarios,” IEEE Transactions on Signal Processing, vol. 59, no. 1, pp. 78–90, Jan 2011.

    Article  MathSciNet  Google Scholar 

  9. C. Sturm and W. Wiesbeck, “Waveform design and signal processing aspects for fusion of wireless communications and radar sensing,” Proceedings of the IEEE, vol. 99, no. 7, pp. 1236–1259, Jul. 2011.

    Article  Google Scholar 

  10. R. F. Tigrek, W. De Heij, and P. V. Genderen, “OFDM Signals as the Radar Waveform to Solve Doppler Ambiguity,” IEEE Transactions On Aerospace And Electronic Systems, vol. 48, no. 1, pp. 130–143, Jan. 2012.

    Article  Google Scholar 

  11. N. Nguyen-Thanh and I. Koo, “Optimal truncated ordered sequential cooperative spectrum sensing in cognitive radio,” IEEE Sensors Journal, vol. 13, no. 11, pp. 4188–4195, Nov. 2013.

    Article  Google Scholar 

  12. S. Bokharaiee, H. H. Nguyen, and E. Shwedyk, “Blind spectrum sensing for ofdm-based cognitive radio systems,” IEEE Transactions on Vehicular Technology, vol. 60, no. 3, pp. 858–871, March 2011.

    Article  Google Scholar 

  13. T. Weiss, J. Hillenbrand, A. Krohn, and F. K. Jondral, “Efficient signaling of spectral resources in spectrum pooling systems,” in SCVT, 2003, pp. 1–6.

  14. R. Xu, M. Chen, J. Zhang, H. Wang, and W. Yu, “Report the sensing results using ofdma in cooperative spectrum sensing,” in 2010 International Conference on Wireless Communications and Signal Processing. Suzhou, China: IEEE, Oct. 21–23 2010, pp. 1–5.

  15. W. Zhang and K. B. Letaief, “Cooperative spectrum sensing with transmit and relay diversity in cognitive radio networks,” IEEE Transaction on Wireless Communications, vol. 12, no. 7, pp. 4761–4766, Jul 2008.

    Article  Google Scholar 

  16. R. Niu, B. Chen, and P. Varshney, “Fusion of decisions transmitted over Rayleigh fading channels in wireless sensor networks,” IEEE Transactions on Signal Processing, vol. 54, no. 3, pp. 1018–1027, Mar. 2006.

    Article  Google Scholar 

  17. M.-S. Alouini and A. J. Goldsmith, “A unified approach for calculating error rates of linearly modulated signals over generalized fading channels,” IEEE Transactions on Communications, vol. 47, no. 9, pp. 1324–1334, Sep. 1999.

    Article  Google Scholar 

  18. M. K. Simon and M.-S. Alouini, “A unified approach to the performance analysis of digital communication over generalized fading channels,” Proceedings of the IEEE, vol. 86, no. 9, pp. 1860–1877, Sep. 1998.

    Article  Google Scholar 

  19. Z. Du, J. Chen, and N. C. Beaulieu, “Error Rate of OFDM Signals on Frequency Selective Nakagami-m Fading Channels,” in IEEE Global Telecommunications Conference, GLOBECOM’04. Dallas, Texas: IEEE, 29 Nov.–3 Dec. 2004, pp. 3994–3998.

  20. Y.-P. Lin and S.-M. Phoong, “Window designs for DFT-based multicarrier systems,” IEEE Transactions on Signal Processing, vol. 53, no. 3, pp. 1015–1024, Mar. 2005.

    Article  MathSciNet  Google Scholar 

  21. A. Papoulis, Probability, Random Variables, and Stochastic Processes, 3rd ed. New York: McGraw-Hill, 1991.

    Google Scholar 

  22. M. Abramowitz and I. A. Stegun, Eds., Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables, 9th ed. New York: Dover, 1970.

    Google Scholar 

  23. J. Goldman, “Detection in the presence of spherically symmetric random vectors,” IEEE Transaction on Information Theory, vol. 22, no. 1, pp. 52–59, Jan. 1976.

    Article  MATH  Google Scholar 

Download references

Acknowledgments

This work is supported by National Nature Science Foundation of China (Nos. 61201217, 61371124), and China Postdoctoral Science Foundation Grant (Nos. 2012M512085, 2013T60911). This paper was presented in part at WCSP 2010 and WCSP 2011.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Renhui Xu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xu, R. Density Function of the Received Signal in OFDM-based Distributed Detection Fusion System. Int J Wireless Inf Networks 22, 357–368 (2015). https://doi.org/10.1007/s10776-015-0281-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10776-015-0281-0

Keywords

Navigation