基于二维特征的认知无线电仿冒授权用户检测

针对认知无线电网络中传统方法信号特征检测性能较弱的问题,提出一种基于二维特征的信号检测方法,并将其用于仿冒授权用户检测。在传统决策理论的基础上,给出一种新的决策参数:零中心归一化瞬时能量绝对值的平均值,将其与盒维数构成一个二维特征参数矢量,作为支持向量机分类器的输入进行信号识别,判断仿冒授权用户攻击是否存在。仿真结果表明,在信噪比达到5 dB时,该算法能完全判别仿冒授权用户攻击是否存在。即使在信噪比为0的环境中,也能在保证对合法授权用户干扰很小的前提下,以较高的概率检测出仿冒授权用户攻击,具有较强的抗噪性能。

Detection of Primary User Emulation in Cognitive Radio Based on Two-dimensional Features

To overcome the shortage of traditional signal feature detection algorithms in Cognitive Radio（CR） networks, a novel detection algorithm for primary user emulation based on two-dimensional features is proposed. It puts forward a new instantaneous characteristics parameter, which is called the average value of the zero-centered and normalized instantaneous energy＇s absolute value, based on traditional decision theory. A two-dimensional vector, which is composed of the new parameter and box dimension, is constructed. This two-dimensional vector is used to judge whether the Primary User Emulation（PUE） attack is present or not, by using classifier based on Support Vector Machine（SVM）. Simulation result shows that, the proposed algorithm can effectively identify primary user attacker while the signal-to-noise ratio is 5 dB, and even in 0 dB environment, it has a high detection probability of PUE attack, in guarantee to have little interference to primary user. Namely, the proposed algorithm has a strong anti-noise performance.