%0 Journal Article
%T 低信噪比下采用广义随机共振的能量检测算法<br>An Energy Detection Algorithm Using Generalized Stochastic Resonance under Low Signal？？to？？Noise Ratios
%A 刘进
%A 李赞
%A 高锐
%J 西安交通大学学报
%D 2015
%R 10.7652/xjtuxb201506005
%X 针对低信噪比下认知无线电中采用能量检测(ED)的频谱感知方法错误概率较大的问题,提出了一种采用广义随机共振的改进的能量检测(IED)算法。该算法首先对接收信号添加一个直流分量,并借助偏移系数确定直流分量的最优幅值,使其与信号中的直流产生广义随机共振;其次,对共振后的信号进行采样和能量累加得到检测统计量,然后根据最小平均错误概率准则确定最佳检测门限并与检测统计量进行比较从而做出判决;最后,从错误概率、样本检测点数和算法复杂度等几个方面给出算法的性能分析。理论推导和仿真结果表明:在信噪比为-15 dB的低信噪比条件下,IED算法的错误概率性能比传统的ED算法提升了约3 dB;在相同的错误概率条件下,IED算法所需的检测样本点数比ED算法显著减少。<br>An improved energy detection (IED) algorithm using generalized stochastic resonance (GSR) is proposed to reduce the error probability of the energy detection based spectrum sensing method in cognitive radio systems at low signal？？to？？noise ratio (SNR). A direct current (dc) component is added to the received signal and the optimal amplitude of the dc component is determined by the offset coefficient so that the GSR between the dc component and the dc in the signal is generated. A test statistic is obtained by sampling and energy accumulation of resonant signals. The optimal detection threshold is determined based on the minimum average error probability criterion and then a decision result is obtained from a comparison between the test statistic and the detection threshold. The performance analyses of the proposed algorithm are given. The theoretical analyses, simulation results and a comparison with the conventional ED algorithm show that the error probability of the IED algorithm improves by about 3 dB when SNR is -15 dB, and less samples are needed for the IED algorithm under same error probability conditions
%K 认知无线电
%K 频谱感知
%K 能量检测
%K 广义随机共振&lt
%K br&gt
%K cognitive radio
%K spectrum sensing
%K energy detection
%K generalized stochastic resonance
%U http://zkxb.xjtu.edu.cn/oa/DArticle.aspx?type=view&id=201506005