应用小波变换的认知无线电频谱检测  

Spectrum sensing based on wavelet transform for cognitive radio

作　　者：李晓艳[1] 张海林[1] 胡飞[2] 

机构地区：[1]西安电子科技大学综合业务网国家重点实验室 [2]阿拉巴马大学电子与计算机工程系

出　　处：《计算机工程与应用》2011年第13期4-7,共4页Computer Engineering and Applications

基　　金：国家自然科学基金No.60772317;综合业务网国家重点实验室专项基金(No.ISN090105);中央高校基本科研业务费专项资金资助(No.72105377)~~

摘　　要：频谱检测是认知无线电的核心问题之一,利用小波变换对接收信号的功率谱密度(PSD)进行奇异点检测,为了在低信噪比条件下有效地检测空闲频谱,分两步去除噪声在检测中的影响。首先利用噪声与信号奇异点的小波变换模极大值在不同尺度上具有不同的传播特性,可以去除噪声;再通过小波变换模极大值的衰减计算Lipschitz指数,去掉与信号奇异点具有不同Lip-schitz指数的噪声。最后依据剩下的奇异点将PSD划分为多个子带,利用带通滤波器估计每个子带的PSD水平,最终确定出空闲频谱。仿真结果证明了该方法的可行性。Spectrum sensing is one of the core issues in cognitive radio.This paper uses the wavelet transform to detect the singularities of the received signal’s Power Spectrum Density（PSD）.In order to sense the spectrum holes efficiently in low signal-to-noise situation,two-step method is proposed to remove the noise during the spectrum sense.Firstly,propagation characteristics of the wavelet transform modulus maxima of signal are contrary to the noise on different scales,and the noise can be eliminated from the signal.Secondly,the Lipschitz exponent is calculated from the decay of the wavelet transform modulus maxima.Then the noise which has different Lipschitz exponent from the singularities can be removed.Finally the frequency band is divided into several subbands according to the singularities and the bandpass filter is used to estimate the PSD level of each subbands.The spectrum holes are defined ultimately.Simulation results show that the proposed method is correct and validated

关 键 词：认知无线电 频谱检测.小波变换 去噪 

分 类 号：TN929.52[电子电信—通信与信息系统]