Machine Learning for Position Prediction and Determination in Aerial Base Station System

Abstract

A novel framework for dynamic 3-D deployment of unmanned aerial vehicle (UAV) in the aerial base station system (ABSS) that based on the machine learning algorithms is proposed. In the framework, the UAV is deployed as an aerial base station to serve a group of ground users and is placed based on the prediction of the users' mobility. The joint problem of prediction of users' track and 3-D deployment of the UAV is formulated for maximizing the sum transmit rate. A two-step approach is proposed for predicting the movement of users and for determining the dynamic 3-D placement of the UAV. Firstly, an echo state network (ESN) based prediction algorithm is utilized for predicting the future positions of users based on the real-world datasets collected from Twitter. Secondly, an iterative K-Means based algorithm is proposed for obtaining the optimal placement of UAV at each time slot based on the output of ESN model. Numerical results are illustrated for showing the superiority of the proposed algorithm over the prevalent algorithm on prediction tasks. The accuracy and efficiency of the proposed framework are also investigated. Additionally, compared with static placement of the UAV, the advantage of dynamic 3-D deployment is demonstrated.