低信噪比条件下基于随机共振的感知方法与性能分析

doi:10.3969/j.issn.0372-2112.2013.09.002

电子学报 ›› 2013, Vol. 41 ›› Issue (9): 1672-1679.DOI: 10.3969/j.issn.0372-2112.2013.09.002

低信噪比条件下基于随机共振的感知方法与性能分析

高锐, 李赞, 吴利平, 李群伟, 齐佩汉

西安电子科技大学ISN国家重点实验室, 陕西西安 710071

收稿日期:2012-07-02 修回日期:2013-03-25 出版日期:2013-09-25
- 作者简介:
- 高锐 男,1986年5月出生于江苏省扬州市,现为西安电子科技大学通信工程学院军事通信学专业博士研究生,主要研究方向为无线通信、微弱信号检测. E-mail:gaoruiyangzhou@163.com;李赞 女,1975年7月出生于陕西省西安市,现为西安电子科技大学ISN国家重点实验室教授、博士生导师,主要研究方向为无线通信、数字信号处理、流星余迹通信.
- 基金资助:
- 国家自然科学基金 (No.61072070); 教育部博士学科点基金 (No.20110203110011); 教育部基础科研业务费 (No.72124338); 陕西省自然基金重点项目 (No.2012JZ8002); ISN国家重点实验室自主课题 (No.ISN1101002); 高等学校学科创新引智计划 (No.B08038)

A Spectrum Sensing Method and Performance Analysis Based on Stochastic Resonance Under Low SNR

GAO Rui, LI Zan, WU Li-ping, LI Qun-wei, QI Pei-han

State Key Laboratory of ISN, Xidian University, Xi'an, Shaanxi 710071, China

Received:2012-07-02 Revised:2013-03-25 Online:2013-09-25 Published:2013-09-25

摘要/Abstract

摘要： 针对认知网络实际环境中常呈现出噪声高动态变化、低信噪比特征,无法快速准确进行频谱感知的问题,本文将物理学非线性领域中的随机共振理论引入到频谱感知中,提出了一种基于广义随机共振的能量检测算法.该算法引入匹配噪声,通过匹配非线性系统、噪声和信号三者的关系,从而改变能量检测统计量的分布,有效地检测信号的存在性.本文从理论上推导了最佳匹配噪声的表达式,并得到了检测性能、受噪声不确定度的影响、感知时间等方面的重要理论结论.仿真结果验证了理论推导的正确性,表明所提算法能够在信噪比为-20dB等低信噪比条件下较现有能量检测算法提高3dB以上,且具有感知速度快、受噪声不确定度影响小等特点.

关键词: 频谱感知, 随机共振, 能量检测, 低信噪比, 噪声不确定度

Abstract: According to the spectrum sensing problem under low signal-noise ratio (SNR) and dynamic noise in cognitive radio(CR)networks,this paper introduced nonlinear stochastic resonance(SR)of physics into spectrum sensing,and proposed an energy detection(ED)based on generalized stochastic resonance(GSR).For the proposed algorithm,SR noise was added to make nonlinear system,signal and noise matched,which modifies the probability distribution of the detection statistics,and confirms the existence of the signal effectively.This paper drive the probability density unction(PDF)of matched noise,and get some significant conclusions about the performance,effect of noise uncertainty and sensing time of the proposed algorithm.The simulation results validate the theory,and show that the proposed algorithm can improve the performance of existing energy detection at least 3dB under low SNR.The proposed algorithm also has less sensing time,low complexity and can effectively overcome the influence of the noise uncertainty.

Key words: spectrum sensing, stochastic resonance, energy detection, low SNR, noise uncertainty

中图分类号:

TN911.23

高锐, 李赞, 吴利平, 李群伟, 齐佩汉. 低信噪比条件下基于随机共振的感知方法与性能分析[J]. 电子学报, 2013, 41(9): 1672-1679.

GAO Rui, LI Zan, WU Li-ping, LI Qun-wei, QI Pei-han. A Spectrum Sensing Method and Performance Analysis Based on Stochastic Resonance Under Low SNR[J]. Acta Electronica Sinica, 2013, 41(9): 1672-1679.

参考文献

[1] Mitola J,Gerald Q,Maguire J R.Cognitive radios.making software radios more personal[J].IEEE Personal Communications,1999,6(4):13-18.

[2] H Urkowitz.Energy detection of unknown deterministic signals[J].Proc IEEE,1967,55(4):523-531.

[3] J Theiler,B R Foy.Effect of signal contamination in matched-filter detection of the signal on a cluttered background[J].IEEE Geoscience and Remote Sensing Letters,2006,3(1):98-102.

[4] J Lunden,S A Kassam,V Koivunen.Robust nonparametric cyclic correlation-based spectrum sensing for cognitive radio[J].IEEE Transactions on Signal Processing,2010,58(1):38-52.

[5] R Benzi,A Sutera,A Vulpiani.The mechanism of stochastic resonance[J].Phys A,1981,14(11):L453-L457.

[6] R P Morse,P Roper.Enhanced coding in a cochlear-implant model using additive noise:Aperiod stochastic resonance with tuning[J].Phys Rev E,2000,61(5):5683-5692.

[7] H Chen,P K Varshney,S M Kay,J H Michels.Theory of stochastic resonance effect in signal detection:part1-fixed detectors[J].IEEE transactions on Signal Processing,2007,55(7):3172-3185.

[8] H Chen,P K Varshney.Theory of stochastic resonance effect in signal detection:part2-variable detectors[J].IEEE Transactions on Signal Processing,2008,56(10):5031-5041.

[9] B McNamara,K Wilesenfeld.Theory of stochastic resonance[J].Phys Rev A,Gen Phys,1989,39(9):4854-4869.

[10] 杨定新,胡政,杨拥民.大参数周期信号随机共振解析[J].物理学报,2012年,61(8):1-10. Yang Dingxin,HU zheng,Yang Yongmin.The analysis of stochastic resonance of periodic signal with large parameters[J].Acta Phys Sinica,2011,61(8):1-10.(in Chinese)

[11] M D McDonnell,N G Stocks,C E Pearce,D Abbott.Optimal information transmission in nonlinear arrays through suprathreshold stochastic resonance[J].Physics Letters A,2006,352(3):183-189.

[12] 叶青华,黄海宁,张春华.用于微弱信号检测的随机共振系统设计[J].电子学报,2009,37(1):216-220. YE Qinghua,Huang Haining,Zhang Chunhua.Design of stochastic resonance systems in weak Signal detection[J].Acta Electronica Sinica,2009,37(1):216-220.(in Chinese)

[13] D He,Y Lin,C He,L Jiang.A novel spectrum-sensing techniquein cognitive radio based on stochastic resonance[J].IEEE Trans Veh,Technol,2010,59(4):1680-1688.

[14] V N Hari,G V Anand,A B Premkumar,A S Madhukumar.Design and performance analysis of a signal detector based on suprathreshold stochastic resonance[J].Signal Processing,2012,92(1):1745-1757.

[15] Qunwei Li,Zan Li,Jian Shen,Rui Gao.A novel spectrum sensing method in cognitive radio based on suprathreshold stochastic resonance [A].IEEE International Conferecnc on Communications(ICC 2012)[C] Ottawa:IEEE,2012.4426-4430

[16] R Tandra,A Sahai.Snr walls for signal detection[J].Selected Topics in Signal Processing,IEEE Journal of,2008,2(1):4-17.

[17] A Sonnenschen,P M Fishman.Radiometric detection of spread-spectrum signals in noise uncertain power[J].IEEE Transactions on Aerospace and Electronic Systems,1992,28(3):654-660.

[18] Steven Kay.Noise enhanced detection as a special case of randomization[J].IEEE Signal Processing Letters,2008,15:709-712.

[19] Wei Chen,Jun Wang,Husheng Li,Shaoqian Li.Stochastic resonance noise enhanced spectrum sensing in cognitive radio networks [A].IEEE GLOBECOM 2010 [C].Miami:IEEE,2010.1-6.

低信噪比条件下基于随机共振的感知方法与性能分析

A Spectrum Sensing Method and Performance Analysis Based on Stochastic Resonance Under Low SNR

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