基于随机矩阵和能量双联合的协作频谱感知

Cooperative spectrum sensing based on random matrix and energy dual joint

针对频谱感知算法检测概率较低的问题,提出一种基于随机矩阵和能量双联合的协作频谱感知算法。根据随机矩阵理论,对最大最小特征值(Maximum Minimum Eigenvalue,MME)算法进行优化,并联合能量检测算法进行协作感知。在已知虚警概率情况下,对门限值进行推导。仿真结果表明,提出的算法与特征值之比改进算法、传统MME算法、能量检测算法相比,低虚警概率情况下的检测概率大约提升了43%,低采样点数情况的检测概率大约提升了2%。在保持较高检测概率的同时,信噪比大于-11 dB情况下,相比特征值之比改进算法和传统MME算法,该算法的检测速率有明显提升,在信噪比为-4 dB时,检测速率大约提升了52%。

Aiming at the shortcomings of low detection probability of current spectrum sensing algorithm,a cooperative spectrum sensing algorithm based on random matrix and energy dual joint is proposed.According to the random matrix theory,the Maximum Minimum Eigenvalue(MME)algorithm is optimized and collaborative perception is carried out by combining the energy detection algorithm.In case of knowing false alarm probability,the threshold value is derived.The simulation results show that compared with the improved algorithm of eigenvalue ratio,the traditional MME algorithm and the energy detection algorithm,the detection probability of the proposed algorithm is increased by about 43%in the cases of low false alarm probability,and the detection probability in the cases of low sampling points is increased by about 2%.While maintaining a high detection probability,when the SNR is greater than-11 dB,the detection rate of this algorithm is significantly improved compared with both the improved algorithm of eigenvalue ratio and the traditional MME algorithm.When the SNR is-4 dB,the detection rate has increased by approximately 52%.