A Diversity-Based Algorithm for Energy Detection

Abstract:

Energy detection is the most commonly used algorithm in spectrum sensing. For Unknown signal, the current energy detection has shortcomings on the performance of perceiving the primary users signals in AWGN channel or fading channel. The diversity technology can receive the correlation signals which contain the same information in different branches, then merge and output the signals to reduce the probability of deep fading at the receiving terminal greatly. Therefore, we introduce diversity into the energy detection to improve the ability of detection. Simulation result shows that, it can improve the cognitive ability and obtain diversity gain through reducing the impact of fading. With the increase of diversity numbers, our method obtains lower probability of false alarm detection and performs much better than the traditional energy detection.

• Periodical: Advanced Materials Research (Volume 462)
• Pages: 336-343
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.462.336
• Online since: February 2012
• Authors: Zhen Quan Qin, De Long Liu, Wei Feng Sun, Yan Hu, Huang Hui
• Keywords: Diversity Reception, Energy Detection, Spectrum Sensing

References

[1] G. Staple, K. Werbach. The end of spectrum scarcity. IEEE Spectrum. 2004(3): 48-52.

DOI: 10.1109/mspec.2004.1270548

[2] J. Mitola, G. Q. Maquire. Cognitive Radio: Making Software Radios More Personal. IEEE Personal Communications. 1999(4): 13-18.

DOI: 10.1109/98.788210

[3] Y. M. Kim, G. Zheng, S. H. Sohn, and J. M. Kim. An alternative energy detection using sliding window for cognitive radio system. IEEE ICACT. 2008: 481–485.

DOI: 10.1109/icact.2008.4493807

[4] B. Shent, L. Huang, C. Zhao, Z. Zhou, and K. Kwak, Energy detection based spectrum sensing for cognitive radios in noise of uncertain power. IEEE ISCIT. 2008: 628–633.

DOI: 10.1109/iscit.2008.4700269

[5] Y. Zhuan, G. Memik, J. Grosspietsch. Energy Detection Using Estimated Noise Variance for Spectrum Sensing in Cognitive Radio Networks. IEEE WCNC. 2008: 711-716.

DOI: 10.1109/wcnc.2008.131

[6] D. Cabric, S. M. Mishra, R. W. Brodersen. Implementation issues in Spectrum Sensing for Cognitive Radios. Asilomar Conference on Signals, Systems, and Computers. 2004: 772-776.

DOI: 10.1109/acssc.2004.1399240

[7] H. Urkowitz. Energy detection of unknown deterministic signals. Proceedings of IEEE. 1967(55): 523-231.

DOI: 10.1109/proc.1967.5573

[8] A. Neasmith, N C Btaulieu. New results on selection diversity. Communication IEEE Transactions on. 1998(5): 695-700.

[9] W. C, Y. LEE. Mobile Communication Engineering. New York: McGraw-Hill, (1982).

[10] T. Yucek, H. Arslan. A Survey of Spectrum Sensing Algorithms for Cognitive Radio Applications. IEEE Communications Surveys & Tutorials, vol 11, No 1, (2009).

DOI: 10.1109/surv.2009.090109