Weighted Graph Based Clustering and Local Mobility Management for Dense Small Cell Network with X2 Interface

Along with the surge in mobile data, dense small cell network has become an effective method to improve system capacity and spectrum efficiency. However, because more small cells are deployed, the interference among dense small cells exacerbates. It also makes frequent handover for mobile users (UEs), which brings a great deal of signaling overhead to the core network. In order to solve the problems of interference and frequent handover, a novel clustering scheme for dense small cell network is proposed in this paper. The scheme is based on the weighted graph. First, we present a dense small cell clustering model based on X2 interface to minimize core network signaling overhead. To improve the usability of the model, we model the system as an undirected weighted graph. Then we propose the maximum benefit merging algorithm to reduce the complexity. This method enables adjacent small cells to cooperate and form virtual cellular cluster according to handover statistics information. Then we select cluster head (CH) according to certain rule in each cluster. Cluster head acts as the mobility anchor, managing the handovers between cluster members. This can reduce core network signaling overhead and the interference among small cells effectively. Compared with the 3GPP handover algorithm, the proposed clustering model in this paper can reduce the signaling overhead more than 70%. The simulation results show that the proposed clustering model can effectively cluster the dense small cell.