User Association and Power Allocation Based on Unsupervised Graph Model in Ultra-Dense Network
Kunlin Hou, Qinzhen Xu, Xiangyu Zhang, Yongming Huang, Luxi Yang
Abstract
Ultra-Dense Network (UDN) has become a key technology in 5G communication systems. By deploying low power micro base stations (BSs) densely and flexibly to reduce the distance between access nodes and user equipments (UEs), the spectrum efficiency and energy efficiency of the network can be improved effectively. But at the same time, it also poses new challenges for power control and user association. In this paper, the joint optimization problem of user association and power control of the downlink in a UDN scenario is considered. To make full use of channel information, we build a graph model with UEs as nodes and leverage the Spectral Clustering algorithm for user association. Then we build a graph model with BSs as nodes for the UDN scenario and train an unsupervised graph neural network to achieve power allocation. The analysis of the simulation results verifies the convergence of the proposed scheme which is effective in achieving user association and power control in UDN.