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A Comparison of Energy Detectability Models for Cognitive Radios in Fading Environments

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Abstract

In this work, we first analyze the accuracy of different energy detector models in approximating the exact solution in AWGN. These models motivate us to develop approximation analysis to address energy detection for fading channels. Our analysis develops approximation that has almost the same performance as the exact solution in Rayleigh channels. Our new model is simple enough to derive the relationship between the required number of samples (N) and the signal-to-noise ratio for a single Rayleigh channel similar to the one obtained for AWGN channels. We also define a fading margin for link budget calculations that relates N in fading channels to AWGN channels. Furthermore, we analyze the impact of multiple antennas for cognitive radios considering two receiver diversity schemes and quantify the improvement in performance regarding this margin. All the analytical results derived in this paper are verified by simulations. Finally, we have implemented and verified energy detection models in our multiple antenna wireless testbed.

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References

  1. Mitola, J., III. (1999). Cognitive radio for flexible mobile multimedia communication. In Proceedings of IEEE International Workshop on Mobile Multimedia Communications (pp. 3–10).

  2. Mills R. F., Prescott G. E. (1996) A comparison of various radiometer detection models. IEEE Transactions on Aerospace and Electronic Systems 32(1): 467–473

    Article  Google Scholar 

  3. Edell, J. (1976). Wideband, noncoherent, frequency-hopped waveforms and their hybrids in low-probability of intercept communications. Report Naval Research Laboratory (NRL) 8025.

  4. Akyildiz I. F., Lee W. Y., Vuran M. C., Mohanty S. (2006) NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey. Computer Networks Journal (Elsevier) 50: 2127–2159

    Article  MATH  Google Scholar 

  5. Urkowitz H. (1967) Energy detection of unknown deterministic signals. Proceedings of IEEE 55: 223–231

    Article  Google Scholar 

  6. Digham, F. F., Alouini, M. S. & Simon, M. K. (2003). On the energy detection of unknown signals over fading channels. In Proceedings of IEEE (pp. 3575–3579).

  7. Digham F.F., AlouiniM.S. Simon M.K. (2007) On the energy detection of unknown signals over fading channels. IEEE Transactions on Communications, 55(1): 21–24

    Article  Google Scholar 

  8. Pandharipande, A. & Linnartz, J.-P. M. G. (2007). Performance analysis of primary user detection in a multiple antenna cognitive radio. In IEEE International Conference on Communications (pp. 6482–6486).

  9. Cabric, D., Tkachenko, A. & Brodersen, R. W. (2006). Experimental study of spectrum sensing beased on energy detection and network cooperation. In Proceedings on ACM International workshop on Technology and Policy for Accessing Spectrum.

  10. Nuttall A. H. (1975) Some integrals involving the Q M function. IEEE Transactions on Information Theory 21: 95–96

    Article  MathSciNet  MATH  Google Scholar 

  11. Torrieri D. J. (1992) Principles of secure communication systems. Artech House, Boston, MA

    Google Scholar 

  12. Kostylev V. I. (2002) Energy detection of a signal with random amplitude. IEEE International Conference on Communications, ICC 2002 3: 1606–1610

    Google Scholar 

  13. Ghasemi, A. & Sousa, E. S. (2005) Colloborative spectrum sensing for opportunistic access in fading environments. In Proceedings of IEEE Dynamic Spectrum Access Networks (pp. 131–136).

  14. Bhattacharya R., Waymire E. C. (2007) A basic course in probability theory. Springer, New York, NY

    MATH  Google Scholar 

  15. ChungK.-L. Yan W.-M. (1997) The complex householder transform. IEEE Transactions on Signal Processing, 45(9): 2374–2376

    Article  Google Scholar 

  16. Dohler M., Arndt M. (1997) Inverse incomplete gamma function and its application. IEEE Electronics Letters 42(1): 35–36

    Article  Google Scholar 

  17. Gradshteyn I. S., Ryzhik I. M. (2007) Table of integrals, series, and products. Academic Press, San Diego, CA

    MATH  Google Scholar 

  18. Kim, D. & Torlak, M. (2008). Rapid prototyping of a cost effective and flexible 44 MIMO testbed. In Proceedings of IEEE Sensor Array and Multichannel Signal Processing Workshop (pp. 5–8), 21–23 July 2008. Darmstadt, Germany.

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Authors and Affiliations

  1. R & D Department, Turk Telekom, AS, Ankara, Turkey

    Selami Ciftci

  2. Department of Electrical Engineering, The University of Texas at Dallas, Richardson, TX, 75080, USA

    Murat Torlak

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  1. Selami Ciftci
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  2. Murat Torlak
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Correspondence to Murat Torlak.

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Ciftci, S., Torlak, M. A Comparison of Energy Detectability Models for Cognitive Radios in Fading Environments. Wireless Pers Commun 68, 553–574 (2013). https://doi.org/10.1007/s11277-011-0468-3

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