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

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

学术论文

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

作者信息 +

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

Author information +

文章历史 +

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.

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.

导出引用

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. https://doi.org/10.3969/j.issn.0372-2112.2013.09.002

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. https://doi.org/10.3969/j.issn.0372-2112.2013.09.002

中图分类号： TN911.23

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基金

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)