An adaptive clustering approach for small cell in ultra-dense networks

Abstract

As one of the key technique to realize the large network capacity in the fifth generation mobile communication networks (5G), ultra-dense networks (UDNs) is centralized deployment of small cell stations (SCSs) which is caused interference problem and complex network structure, hinder the application of existing radio resource management (RRM) and interference management (IM) scheme on UDNs directly. Clustered RRM and IM provides a feasibility mechanism to solve this problem. However, how to properly form SCS cluster has not been well studied. We believe that small cells clustering is an effective method to simplify the topology of ultra-dense network. The trend of clustering approach is lower complexity and user-centric. In this paper, we propose a user-centric adaptive small-cell clustering scheme based on improved K-means algorithm. Simulation results show that the proposed scheme can dynamic adjust the size and number of small cell clusters according to user's signal to interference plus noise ratio (SINR), and reduce the computational complexity in the clustering process effectively.