An Efficiency-Improved Clustering Algorithm Based on KNN Under Ultra-Dense Network
Yanxia Liang, Changyin Sun, Jing Jiang, Xin Liu, Hua He, Yongbin Xie
Abstract
Ultra-Dense Network (UDN) is one of the key techniques for the next generation of mobile network due to providing high system throughput. However, severe interference often occurs in UDN, which greatly impact the data rates of cell-edge users. User-centric wireless access virtualization has been widely adopted in UDN to mitigate the interference of cell-edge users by sharing resources and eliminating cell boundary. However, it's only effective for moderate scale networks. Moreover, the efficiency needs further improvement. In this paper, we study effective cooperative clustering method for large scale UDN with less computations in order to improve the throughput of cell-edge users. We formulate a convex optimization problem in which the objective is to maximize the system throughput with overlapping virtual cells. We propose a clustering method to solve this optimization problem. We design a fast-convergent iterative algorithm called K-Nearest Neighbor (KNN) algorithm to perform users clustering. Simulation results show that our proposed algorithm has better throughput performance for both average and cell-edge users. Especially, the per-carrier throughput is improved, which leads to more serviceable users with limited resources.