一种基于特征值和级联聚类的协作频谱感知方法

为了提高低信噪比(Signal-to-Noise Ratio, SNR)下频谱感知的性能，使用模糊C均值(Fuzzy C-means, FCM)和高斯混合模型(Gaussian Mixture Model, GMM),提出了一种基于特征值和级联聚类的协作频谱感知方法。从接收信号的协方差矩阵中提取特征值构造特征向量，通过在三维空间中执行聚类得到信道是否可用的分类模型，此过程无需获得主用户(Primary User, PU)信号以及噪声功率的先验信息，避免了复杂的门限计算。FCM聚类用于优化GMM聚类的初始参数，有效解决了在低SNR下GMM容易陷入局部最小值的问题。仿真结果表明，该方法降低了GMM的收敛时间并提高了模型分类的准确性，与其他主流方法相比能够有效提升频谱感知的性能。

A Cooperative Spectrum Sensing Method Based on Eigenvalue and Cascade Clustering

In order to improve the performance of spectrum sensing in low signal to noise ratio(SNR),a cooperative spectrum sensing method based on eigenvalue and cascade clustering is proposed by using Fuzzy C-means(FCM) and Gaussian Mixture Model(GMM).The feature vectors are constructed by extracting the eigenvalues from the covariance matrix of the received signals,and the classification model of whether the channel is available is obtained by performing clustering in three-dimensional space.This process does not need to obtain the prior information of the primary user(PU) signal and the noise power,which avoids the complex threshold calculation.FCM clustering is used to optimize the initial parameters of GMM clustering,which effectively solves the problem that GMM is prone to fall into local minimum in low SNR.Simulation results show that the proposed method both reduces the convergence time of GMM and improves the accuracy of model classification.Compared with other mainstream methods,it can effectively improve the spectrum sensing performance.