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Testing and Evaluation of Modified Dynamic threshold Energy Detection Algorithm for CR Sensing Applications

Authors(3):

T. Srinivas , B. Jagadeesh Babu, G.Bheemeswara Rao
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Nowadays Energy detection via threshold is a complex and multifaceted issue in Cognitive Radio sensing applications. A Cognitive radio (CR) is all time monitoring smart radio which detects available channels in wireless spectrum. The important features of CR are Spectrum mobility, Spectrum sharing, Sensing-based Spectrum sharing and spectrum reuse. CR sensing is used to detect and locate unused area of spectrum and sharing it among many users by following the protocols of EMI & EMC (if possible also senses empty spectrum.). Hence Primary users (PU) detection is Vital for proper spectrum usage. The widely used Spectrum-sensing method is Transmitter detection. It may be of three kinds. It may be usually matched filter detection, sometimes Energy detection and in special cases it is Cyclo stationary featured detection. Among them Matched filter configuration is provided by maximizing peak signal to mean noise ratio but it results many demerits whereas energy detection is the best alternative. The conventional energy detection technique uses fixed threshold. Measurement of RSS (Received Signal strength) in terms of power indicates whether signal is present or not. So Threshold indicates the optimum (minimum) level of signal power for detection. Noise variance information is required to design the proposed energy detector. This is the simple process involved in energy detection. If we donít know noise power then SNR (Signal to Noise ratio) walls problem comes into picture due to noise uncertainty. This uncertainty obtains poor and un-optimized performance in several cases.

The main Objective of this paper is to address the above discussed problem by implementing a new efficient energy detector to provide best performance in CR sensing applications. i.e. it uses dynamic threshold which uses two threshold levels. The required two threshold values are determined by noise uncertainty factor (NUF). The Receiver operating characteristic (ROC), Monte-Carlo simulation provided the promising results. This algorithm can be suited for various sensing applications with minute modifications. Its main merit is it does not need any information of the signal, estimation of noise and channel powers.

T. Srinivas , B. Jagadeesh Babu, G.Bheemeswara Rao

CR, Energy Detection, Noise Uncertainty factor, Probability of detection, Probability of false alarm, Primary user, ROC curve, SNR-wall, Spectrum sensing, Threshold .

  1. K. Milne, “Theoretical performance of a complex cross-correlator with gaussian signals,” IEEE Proceedings F Radar and Signal Processing, vol.140, no. 1, pp. 81–88, 1993..
  2. A. Papoulis, Probability, Random Variables and Stochastic Processes. McGraw-Hill, 3 ed.1991.
  3. M. Naraghi-Pour and T. Ikuma, “Autocorrelation-based spectrum sensing for cognitive radios,” IEEE Trans. Veh. Technol., vol.59, no. 2, pp. 718–733, feb. 2010. .
  4. F. F. Digham, M. S. Alouini, and M. K. Simon,” On the energy detection of unknown Signals over fading channels,” Proceedings of IEEE, pp. 3575-3579, May 2003.
  5. 2013A. Sahai, N. Hoven, R. Tandra, “Some fundamental limits in cognitive radio,” Allerton Conference on communication, control and computing, Oct. 2004.
  6. W. Jouini, “Energy detection limits under Log-Normal approximated noise uncertainty,” IEEE Signal Processing Letters, vol. 18, pp. 423–426, July 2011.
  7. S. M. Kay, “Fundamentals of Statistical Signal Processing, Volume II: Detection Theory”. Prentice Hall, 1998.
  8. A. Bagwari, and G. Singh Tomar, “Improved Spectrum Sensing Technique using Multiple Energy Detectors for Cognitive Radio Networks,” International Journal of Computer Applications (0975–8887), Vol. 62, pp. 11-21, Jan..
  9. X. Hue, X. Xie, T. Song and W. Lei, “An Algorithm for Energy Detection Based on Noise Variance Estimation under Noise Uncertainty,” IEEE International Conference on Communication and Technology, pp. 1-5, Nov. 2012.
  10. Z. Pei, Q. Tong, L. Wang, and J. Zhang, “A Median Filter Method for Image Noise Variance Estimation”, International Conference on Information Technology and Computer Science (ITCS 2010), July 2010.
  11. H. Urkowitz, “Energy detection of unknown deterministic signals,” Proc. IEEE, vol. 55, pp. 523–531, Apr. 1967.
  12. F. Digham, M. Alouini, and M. Simon, “On the energy detection of unknown signals over fading channels,” IEEE Transactions on Communications, vol. 55, pp. 21–24, Jan. 2007.
  13. G. Yu, C. and W. Xi, “A Novel Energy Detection Scheme Based on Dynamic Threshold in Cognitive Radio Systems,” Journal of Computational Information Systems, Vol. 8, pp.2245- 2252, Mar. 2012.
  14. R. Tandra and A. Sahai, “SNR Walls for Signal Detection”, IEEE Journal of Selected Topics in Signal Processing, Vol.2, pp. 4-17, Feb. 2008.
  15. T. Yücek and H. Arslan, “A survey of spectrum sensing algorithms for cognitive radio applications,” IEEE Communications Surveys and Tutorials, vol. 11, pp. 116–130, First Quarter 2009
  16. www.en.wikipedia.com

Publication Details

Published in : Volume 2 | Issue 3 | May-June - 2016
Date of Publication Print ISSN Online ISSN
2016-06-30 2395-1990 2394-4099
Page(s) Manuscript Number   Publisher
594-599 IJSRSET1623172   Technoscience Academy

Cite This Article

T. Srinivas , B. Jagadeesh Babu, G.Bheemeswara Rao, "Testing and Evaluation of Modified Dynamic threshold Energy Detection Algorithm for CR Sensing Applications", International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 2, Issue 3, pp.594-599, May-June-2016.
URL : http://ijsrset.com/IJSRSET1623172.php