吉林大学学报(工学版) ›› 2015, Vol. 45 ›› Issue (4): 1297-1303.doi: 10.13229/j.cnki.jdxbgxb201504039

Previous Articles     Next Articles

Covariance matrix spectrum sensing algorithm based on stochastic resonance and MIMO technology

ZHAO Xiao-hui, LI Xiao-yan   

  1. College of Communication Engineering, Jilin University, Changchun 130012,China
  • Received:2013-06-17 Online:2015-07-01 Published:2015-07-01

RICH HTML

0   

PDF (PC)

189

Abstract: In order to achieve reliable spectrum sensing by users under the condition of low Signal-to-Noise Ratio (SNR) and fading channel in cognitive radio systems, a new covariance matrix spectrum sensing algorithm based on stochastic resonance and Multi-input-Multi-output (MIMO) technology is proposed; and the calculation method of the decision threshold is derived. Simulations show that the performance of the proposed algorithm is batter than the algorithm without introduction of the stochastic resonance and MIMO technology. When the number of antennae is large enough, the proposed method can achieve good performance at low SNR.

Key words: communications, spectrum sensing, stochastic resonance, MIMO technology, covariance matrix

CLC Number: 

  • TN929
[1] Mitola J, Magutre G Q. Cognitive radio: making software radios more personal[J]. IEEE Personal Communications, 1999, 6(4):13-17.
[2] Mitola J. Cognitive radio: an intergrated agent architecture for software defined radio[D]. Kista Sweden: Royal Institute of Technology, 2000.
[3] McNamara B, Wilesenfeld K. Theory of stochastic resonance[J]. Phys Rev A, 1989, 39(9): 4854-4869.
[4] Benzi R, Sutera A, Vulpiani A. The mechanism of stochastic resonance[J]. J Phys A: Math Gen, 1981, 14(11): 453-457.
[5] Gammaitoni L, Hanggi P, Jung P, et al. Stochastic resonace[J]. Reviews of Modern Physics, 1998, 70(1):223-287.
[6] He D, Li W H, Zhu F S, et al. An enhanced covariance spectrum sensing technique based on stochastic resonance in cognitive radio networks[C]∥IEEE International Symposium on Circuits and Systems (ISCAS), Seoul, 2012: 818-821.
[7] He D, Lin Y P, He C, et al. A novel spectrum-sensing techique in cognitive radio based on stochastic resonance[J]. IEEE Transactions on Vehicular Technology, 2010, 59(4):1680-1688.
[8] Chen W, Wang J, Li H S, et al. Stochastic resonance noise enhanced spectrum sensing in cognitive radio networks[C]∥IEEE Global Telecommunications Conference (GLOBECOM),Miami,2010.
[9] Zheng K, Li H S, Djouadi S M, et al. Spectrum sensing in low snr regime via stochastic resonance[C]∥The 44th Annual Conference on Information Sciences and Systems (CISS), Princeton, 2010.
[10] Mietzner J, Schober R, Lampe L, et al. Multiple-antenna techniques for wireless communications—a comprehensive literature survey[J]. IEEE Communications Surveys and Tutorials, 2009, 11(2):87-105.
[11] Zeng Y H, Liang Y C. Eigenvalue-based spectrum sensing algorithms for cognitive radio[J]. IEEE Transactions on Communications, 2009, 57(6): 1784-1793.
[12] Soltanmohammadi E M, Orooji M, Naraghi-Pour M. Spectrum sensing over MIMO channels using generalized likelihood ratio tests[J]. IEEE Signal Processing Letters, 2013, 20(5):439-442.
[13] Zeng Y H, Liang Y C. Spectrum-sensing algorithms for cognitive radio based on statistical covariances[J]. IEEE Transactions on Vehicular Technology, 2009, 58(4):1804-1815.
[14] Wang G F, Zhang H R, Zhang F Q, et al. Scale transformation for detecting weak periodic signal of stochastic resonance[C]∥International Conference on Intelligent Computing and Integrated Systems (ICISS),Guilin, China,2010.
[15] Wang J H, Xiao Q, Li X. The high-frequency weak signal detection based on stochastic resonance[C]∥International Conference on Test and Measurement, Hong Kong, 2009.
[16] 冷永刚, 王太勇. 二次采样用于随机共振从强噪声中提取弱信号的数值研究[J]. 物理学报,2003,52(10): 2432-2437. Leng Yong-gang, Wang Tai-yong. Numerical research of twice sampling stochastic resonance for the detection of a weak signal submerged in a heavy noise[J]. Acta Physica Sinica, 2003, 52(10):2432-2437.
[17] Anishchenko V S, Astakhov V, Neiman A, et al. Nonlinear Dynamics of Chaotic and Stochastic Systems[M]. Berlin: Springer-Verlag, 2002.
[18] 冷永刚,王太勇,郭焱. 级联双稳定系统的随机共振特性[J].物理学报, 2005, 54(3): 1118-1125. Leng Yong-gang, Wang Tai-yong, Guo Yan. Stochastic resonance behaviors of bistable systems connected in series[J]. Acta Physica Sinica, 2005, 54(3):1118-1125.
[1] CHEN Yong-heng,LIU Fang-hong,CAO Ning-bo. Analysis of conflict factors between pedestrians and channelized right turn vehicles at signalized intersections [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(6): 1669-1676.
[2] LIU Xiang-yu, YANG Qing-fang, KUI Hai-lin. Traffic guidance cell division based on random walk algorithm [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1380-1386.
[3] LIU Zhao-hui, WANG Chao, LYU Wen-hong, GUAN Xin. Identification of data characteristics of vehicle running status parameters by nonlinear dynamic analysis [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1405-1410.
[4] LUAN Xin, DENG Wei, CHENG Lin, CHEN Xin-yuan. Mixed Logit model for understanding travel mode choice behavior of megalopolitan residents [J]. 吉林大学学报(工学版), 2018, 48(4): 1029-1036.
[5] CHEN Yong-heng, LIU Xin-shan, XIONG Shuai, WANG Kun-wei, SHEN Yao, YANG Shao-hui. Variable speed limit control under snow and ice conditions for urban expressway in junction bottleneck area [J]. 吉林大学学报(工学版), 2018, 48(3): 677-687.
[6] WANG Zhan-zhong, LU Yue, LIU Xiao-feng, ZHAO Li-ying. Improved harmony search algorithm on truck scheduling for cross docking system [J]. 吉林大学学报(工学版), 2018, 48(3): 688-693.
[7] CHEN Song, LI Xian-sheng, REN Yuan-yuan. Adaptive signal control method for intersection with hook-turn buses [J]. 吉林大学学报(工学版), 2018, 48(2): 423-429.
[8] SU Shu-jie, HE Lu. Transient dynamic congestion evacuation model of pedestrian at walk traffic planning crossroads [J]. 吉林大学学报(工学版), 2018, 48(2): 440-447.
[9] MENG Pin-chao, LI Xue-yuan, JIA Hong-fei, LI Yan-zhong. Short-time rail transit passenger flow real-time prediction based on moving average [J]. 吉林大学学报(工学版), 2018, 48(2): 448-453.
[10] WANG Zhan-zhong, ZHAO Li-ying, JIAO Yu-Ling, CAO Ning-bo. Social force model of pedestrian-bike mixed flow at signalized crosswalk [J]. 吉林大学学报(工学版), 2018, 48(1): 89-97.
[11] HOU Xian-yao, CHEN Xue-wu. Use of public transit information market segmentation based onattitudinal factors [J]. 吉林大学学报(工学版), 2018, 48(1): 98-104.
[12] GAO Kun, TU Hui-zhao, SHI Heng, LI Zhen-fei. Effect of low visibility in haze weather condition on longitudinal driving behavior in different car-following stages [J]. 吉林大学学报(工学版), 2017, 47(6): 1716-1727.
[13] TAN Li-dong, LIU Dan, LI Wen-jun. Design of bionic compound eye array for traffic accident scene panorama based on fly compound eye [J]. 吉林大学学报(工学版), 2017, 47(6): 1738-1744.
[14] WEI Li-ying, CUI Yu-feng, WEI Jia-rong. Cellular automata model based on local maximum entropy lane-changing rules for electric bicycle flow [J]. 吉林大学学报(工学版), 2017, 47(5): 1436-1445.
[15] YAO Rong-han, ZHANG Xiao-tong, LIAN Lian. Optimization model for controlling reversible approach lanes at signalized intersections [J]. 吉林大学学报(工学版), 2017, 47(4): 1048-1054.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright © Journal of Jilin University(Engineering and Technology Edition), All Rights Reserved.
Tel: +86-431-85095297　Fax: +86-431-85094074　E-mail: xbgxb@jlu.edu.cn
Powered by Beijing Magtech Co. Ltd