Clustering Algorithm in Dense Millimeter Wave Heterogeneous Cellular Networks

Network densification is a key means which is able to solve the rapid growth of mobile communication traffic. Using millimeter wave with rich spectrum resource to accomplish dense coverage of network has the characteristics of small co-frequency interference and large network capacity. However, it also increases the complexity of resource allocation algorithm and system maintenance becomes inconvenient. In view of this, we propose a clustering algorithm for dense millimeter wave heterogeneous cellular network, which takes the signal-to-interference-plus-noise ratio (SINR) in number form as optimization goal, and completes network clustering according to reference base stations and the maximum number of users within cluster. For the integer programming problem which is difficult to solve after modeling, this paper adopts the extended penalty factor and difference of two convex functions programming to obtain solution. The simulation results show that compared with the clustering method which takes SINR in decibel form as optimization goal, the proposed algorithm has higher stability and practicability because it avoids nonlinear operation. And the algorithm has better network performance than K-means clustering method.