On-demand deployment of multiple aerial base stations for traffic offloading and network recovery

Abstract

Unmanned aerial vehicles (UAVs) are being utilized for a wide spectrum of applications in wireless networks leading to attractive business opportunities. In the case of abrupt disruption to existing cellular network operation or infrastructure, e.g., due to an unexpected surge in user demand or a natural disaster, UAVs can be deployed to provide instant recovery via temporary wireless coverage in designated areas. A major challenge is to determine efficiently how many UAVs are needed and where to position them in a relatively large 3D search space. We first consider a discrete set of possible UAV locations distributed in a given 3D space and formulate the problem as a mixed integer linear program (MILP). Owing to the complexity of the MILP problem, we present an effective greedy approach that mimics the behavior of the MILP for small network scenarios and scales efficiently for large network scenarios. Afterwards, we propose and evaluate a more practical approach for multiple UAV deployment in a continuous 3D space, based on an unsupervised learning technique that relies on the notion of electrostatics with repulsion and attraction forces. We present performance results for the proposed algorithm as a function of various system parameters and demonstrate its effectiveness compared to the close-to-optimal greedy approach and its superiority compared to recent related work from the literature.