以用户为中心的超密集网络中窃听用户检测技术

在以用户为中心的超密集网络(User-centric Ultra-Dense Networking,UUDN)中,由于进行信道估计的导频序列长度有限,导致传统的基于信息论准则的窃听检测技术效果不理想甚至完全失效.针对这种情况,提出了一种基于LS-FDC准则的多节点联合检测算法.首先,该方法利用统计学中的线性收缩(Linear Shrinkage,LS)理论,对各节点接收到的样本协方差矩阵进行收缩优化,使其特征分解后更好的拟合总体特征值的分布情况;然后,接入节点组(Access Points Group,APG)中的各节点利用灵活检测准则(Flexible Detection Criterion,FDC)算法进行联合判定是否存在窃听用户;最后,仿真实验与理论分析表明:相较于其他的信源估计检测算法,该算法在较低信噪比和导频序列长度有限时的检测概率显著提高,甚至在导频序列长度小于节点天线数的情况下都能达到很好的检测效果.

Eavesdropper Detection Technology in User-Centric Ultra-Dense Network

In the User-centric Ultra-Dense Network (UUDN), due to the limited length of the pilot sequence for channel estimation, the traditional eavesdropping detection technology based on information theoretic criteria cannot function ideally or is even completely invalid. In view of this, this study proposes a multi-node joint detection algorithm based on the LS-FDC criterion. This method uses the Linear Shrinkage (LS) theory in statistics to shrink and optimize the sample covariance matrix received by each node so that it can better fit the distribution of the overall eigenvalues after eigen-decomposition. The nodes in the Access Point Group (APG) jointly determine whether there are eavesdroppers with the Flexible Detection Criterion (FDC) algorithm. The simulation experiments and theoretical analysis show that compared with other algorithms for signal source estimation and detection, this algorithm has a significantly improved detection probability when the signal-to-noise ratio is low and the pilot sequence length is limited. A good detection effect can still be achieved even when the pilot sequence length is shorter than the number of node antennas.