Location Optimization and User Association For Unmanned Aerial Vehicles Assisted Mobile Networks

Abstract

To meet the rapid growth of diverse traffic demand in mobile networks, unmanned aerial vehicles (UAVs) are proposed to support terrestrial networks by carrying base stations towards demand when needed. However, how to deploy the UAVs effectively and efficiently is a challenging task. In this paper, we investigate this problem from the load balancing perspective. Our goal is to make the traffic loads of UAVs almost equal so that the networks can be stable and robust to unexpected events. In the pre-processing stage, we introduce a clustering method to divide users into several categories so as to initialize the positions of UAVs in the maximal local density areas. Then, we propose a user association strategy to make the UAVs serve almost equal traffic demand, where our optimization task is to minimize the maximum traffic demand of subregions with constraints on the capacity and the shape of subregions. Third, we propose a local search procedure to relocate UAVs based on backtracking line search to refine the load balance among the system. By alternately invoking user association and location algorithms, we can obtain (near) optimum UAV positions. Finally, we adjust the altitude of each UAV to decrease power consumption of the system. Numerical results indicate that our proposal can serve more users compared with the SINR-based strategy. Moreover, the traffic capacity among UAVs and the areas of subregions of UAVs are more balanced.