%0 Journal Article
%A HU Yanjun
%A JIANG Fang
%A LI Liping
%A WANG Yi
%A KANG Ling
%T Clustering Algorithm Combined with Discriminant Function in Ultra Dense Network
%D 
%R 10.13190/j.jbupt.2021-180
%J Journal of Beijing University of Posts and Telecommunications
%P 104-109
%V 45
%N 2
%X 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.
%U https://journal.bupt.edu.cn/EN/10.13190/j.jbupt.2021-180