Learning-Based Joint User-AP Association and Resource Allocation in Ultra Dense Network

With the advantages of Millimeter wave in wireless communication network, the coverage radius and inter-site distance can be further reduced, the ultra dense network (UDN) becomes the mainstream of future networks. The main challenge faced by UDN is the serious inter-site interference, which needs to be carefully addressed by joint user association and resource allocation methods. In this paper, we propose a multi-agent Q-learning based method to jointly optimize the user association and resource allocation in UDN. The deep Q-network is applied to guarantee the convergence of the proposed method. Simulation results reveal the effectiveness of the proposed method and different performances under different simulation parameters are evaluated.

I. Introduction

As one of the key technologies of 5G, Ultra-Dense Network is used to densely deploy a large number of small base stations (SBSs) in hotspots to increase capacity and achieve seamless coverage [1]. However, the dense deployment of SBSs complicates the problems of user association, radio resource allocation, interference control and mobility management [2]. In UDN, SBSs are deployed in overlapping manner, users can choose to be associated with several adjacent SBSs via multi-connectivity solutions [3]. The system performance is greatly influenced by the user association patterns. With the increasing deployment density of SBSs, the network topology becomes very complicated. Moreover, a large number of interference sources with very close signal strength bring huge interference to users. This requires a better resource allocation strategy for interference control.