An New Optimal Decision Threshold Criterion for Broadband-Based Energy Detection with Performance Constraints

Abstract:

We study on the energy detection algorithm of spectrum sensing. The performance of energy detection in spectrum sensing is measured by the false alarm probability and the missing detection probability. In a certain spectrum sharing environment, whether the energy detection algorithm can meet our requirement is depending on the length of observation time and the decision threshold selected. Several experiment results have shown that: when the decision threshold is too low, it will cause much more false alarm; when the decision threshold is too high, it will bring many missing detection. Therefore, it is crucial that choosing an optimal decision threshold according to the length of observation time. In this paper, the closed-form solution of the minimum-cost decision threshold is deduced by using the methodology of mathematical derivation, and a new criterion about selecting an optimal decision threshold is also proposed. At last, the conclusion is proved by simulation.

• Periodical: Advanced Materials Research (Volumes 765-767)
• Pages: 2242-2249
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.765-767.2242
• Online since: September 2013
• Authors: Wen Wen Liu, Long Yuan, Zi Yang Liu, Tao Peng, Wen Bo Wang
• Keywords: Energy Detection, False Alarm Probability, Missing Detection Probability, Observation Time, Optimal Decision Threshold

References

[1] MitOLA J. Cognitive radio：making software radios morepersonal[J]．IEEE Pers Commun，1999, 6(4)：13-18.

[2] Z. Quan, S. Cui, H. V. Poor, and A. Sayed. Collaborative Wideband Sensing for Cognitive Radios[J]. IEEE Signal Processing Magazine, 2009, vol. 25(6): 60-73.

DOI: 10.1109/msp.2008.929296

[3] W. Zhang, R. K. Mallik, and K. B. Leraief, Cooperative spectrum sensing optimization in cognitive radio networks[C], in Proc. IEEE International Conference on Communications (ICC), Beijing, China, 2008: 3411–3415.

DOI: 10.1109/icc.2008.641

[4] Guicai Yu; Chengzhi Long; , Research on energy detection algorithm in cognitive radio systems, Computer Science and Service System (CSSS), 2011 International Conference on , vol., no., pp.3460-3463, 27-29 June (2011).

DOI: 10.1109/csss.2011.5974388

[5] Pu Wang; Sahinoglu, Z.; Man-On Pun; Hongbin Li; , Persymmetric Parametric Adaptive Matched Filter for Multichannel Adaptive Signal Detection, Signal Processing, IEEE Transactions on , vol. 60, no. 6, pp.3322-3328, June (2012).

DOI: 10.1109/tsp.2012.2190411

[6] S. Enserink and D. Cochran, A cyclostationary feature detector, in Proc. 28th Asilomar Conf. Signals, Systems, Computers, Pacific grove, CA, Oct. 1994, pp.806-810.

DOI: 10.1109/acssc.1994.471573

[7] Atapattu S, Tellambura C, Hai Jian. Energy Detection Based Cooperative Spectrum Sensing in Cognitive Radio Networks[J]. Wireless Communications, IEEE Transactions on , 2011, 10(4): 1232-1241.

DOI: 10.1109/twc.2011.012411.100611

[8] Zhang Xiao，Wang Jin-long, Wu Qi-hui. An energy detection algorithm based on cyclic spectrum theory in congnitive radio[J]. Journal of Circuits and Systems V0l. 15(3): 75-80.

[9] Zhuan Ye; Memik, G.; Grosspietsch, J.; , Energy Detection Using Estimated Noise Variance for Spectrum Sensing in Cognitive Radio Networks, Wireless Communications and Networking Conference, 2008. WCNC 2008. IEEE , vol., no., pp.711-716, March 31 2008-April 3 (2008).

DOI: 10.1109/wcnc.2008.131

[10] Liu Zi-yang, Peng Tao, Guo Hai-bo, Wang Wen-bo. Energy detection about broadband signal without priori information of interference system[J]. Journal of Beijing University of Posts and Telecommunications, 2012 , 35 (5): 31-35.