超密集网络中基于判别函数的聚类算法

超密集网络可以通过虚拟小区间的协作来提升用户体验,但由于小区的重叠覆盖使得用户间存在较复杂的干扰问题。因此,提出了一种基于判别函数的聚类算法来缓解强干扰带来吞吐量下降的问题。首先,利用用户间干扰信道的余弦相似度定义用户间的干扰网络;然后,基于干扰网络选出簇头并划分用户,同时为了解决虚拟小区下的模糊用户归属簇问题,以簇间干扰权重之和最大,簇内干扰权重之和最小为原则设计判别函数,对用户进行模糊归类。仿真结果表明,在不增加复杂度的同时,所提算法比其他方法的系统吞吐量提升了10%~30%,对于边缘用户具有一定优势。

Clustering Algorithm Combined with Discriminant Function in Ultra Dense Network

Ultra-dense networks can enhance user experience through collaboration between virtual cells, while the overlapping coverage of cells makes the interference problem between users more complex. Therefore, a discriminant function-based clustering algorithm is proposed to mitigate the throughput degradation problem caused by strong interference. Firstly, the inter-user interference network is defined based on the cosine similarity of inter-user interference channels. Then, cluster heads are selected and users are classified based on the interference network. Meanwhile, to solve the fuzzy user belonging to clusters under virtual cells, a discriminant function is designed to fuzzy-classify users based on the principle of maximizing the sum of inter-cluster interference weights and minimizing the sum of intra-cluster interference weights. The simulation results show that compared with the existing methods, the proposed algorithm improves the system throughput by 10%-30% without increasing the complexity, and has certain advantages for edge users.