Abstract
A new censoring cooperative spectrum sensing scheme based on stochastic resonance (SR) technique in cognitive radio (CR) network is proposed in this paper. The observations of the cooperative secondary users (SUs) whose statistics fall into the censoring interval are processed by SR system in the proposed scheme. The hard fusion and the soft fusion for the censoring cooperative spectrum sensing scheme are analyzed respectively. Theoretical analyses and simulation results show that the proposed censoring cooperative spectrum sensing scheme has the same detection performance as and lower computational complexity than the method that each cooperative SU performs spectrum sensing using SR-based energy detection, and its detection performance is superior to that of the conventional method that all the cooperative SUs perform spectrum sensing using energy detection in hard fusion. In soft fusion, the proposed censoring cooperative spectrum sensing based on equal gain combination can achieve the optimal sensing performance approximately.
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References
Ma J, Li G Y, Juang B H. Signal processing in cognitive radio. Proc IEEE, 2009, 97: 805–823
He D, Lin Y, He C, et al. A novel spectrum-sensing technique in cognitive radio based on stochastic resonance. IEEE Trans Veh Technol, 2010, 59: 1680–1688
Harmer G P, Davis B R, Abbott D. A review of stochastic resonance: circuits and measurement. IEEE Trans Instrum Meas, 2002, 51: 299–309
Mitaim S, Kosko B. Adaptive stochastic resonance. Proc IEEE, 1998, 86: 2152–2183
Zhao Z J, Yao Y, Xu C Y. A time-frequency analysis method using stochastic resonance. In: Proceedings of IEEE Congress on Image and Signal Processing, Sanya, 2008. 569–572
Xu Y, Wang R, Wang F. Detection of amplitude-varied weak signal by genetic adaptive stochastic resonance algorithm. In: Proceedings of the 8th International Conference on Electronic Measurement and Instruments, Xi’an, 2007. 626–630
Ye Q, Huang H, He X, et al. A study on the parameters of bistable stochastic resonance systems and adaptive stochastic resonance. In: Proceedings of IEEE International Conference on Robotics, Intelligent Systems and Signal Processing, Changsha, 2003. 484–488
Dhara A K. Enhancement of signal-to-noise ratio. J Stat Phys, 1997, 87: 251–271
Kay S M. Fundamentals of Statistical Signal Processing, Vol 2: Detection Theory. Sebastopol: Prentice Hall, 1998
Letaief K B, Zhang W. Cooperative communications for cognitive radio networks. Proc IEEE, 2009, 97: 878–893
Zhang W, Mallik R K, Letaief K B. Optimization of cooperative spectrum sensing with energy detection in cognitive radio networks. IEEE Trans Wirel Commun, 2009, 8: 5761–5766
Zeng Y H, Liang Y-C, Hoang A T, et al. A review on spectrum sensing for cognitive radio: challenges and solutions. EURASIP J Adv Signal Process, 2010, 2010: 1–15
Hillenbrand J, Weiss T A, Jondral F K. Calculation of detection and false alarm probabilities in spectrum pooling systems. IEEE Commun Lett, 2005, 9: 349–351
Anishchenko V S, Astakhov V V, Neiman A B, et al. Nonlinear Dynamics of Chaotic and Stochastic Systems: Tutorial and Modern Developments. Berlin/Heidelberg: Spring-Verlag, 2002
Zhu J, Zheng B, Zou Y. Detection time analysis for the multiple-user cooperative spectrum sensing scheme in cognitive radio networks. Sci China Ser F-Inf Sci, 2009, 52: 1915–1925
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Lin, Y., He, C., Jiang, L. et al. Cooperative spectrum sensing based on stochastic resonance in cognitive radio networks. Sci. China Inf. Sci. 57, 1–10 (2014). https://doi.org/10.1007/s11432-013-5010-7
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DOI: https://doi.org/10.1007/s11432-013-5010-7