Machine learning aided trajectory design and power control of multi-UAV

Abstract

A novel framework is proposed for the trajectory design of multiple unmanned aerial vehicles (UAVs) based on the prediction of users' mobility information. The problem of joint trajectory design and power control is formulated for maximizing the instantaneous sum transmit rate while satisfying the rate requirement of users. In an effort to solve this pertinent problem, a three-step approach is proposed which is based on machine learning techniques. Firstly, a multi-agent Q-learning based placement algorithm is proposed for determining the optimal positions of the UAVs based on the initial location of the users. Secondly, in an effort to determine the mobility information of users based on a real dateset, their position data is collected from Twitter to describe the anonymous user- trajectories in the physical world. In the meantime, an echo state network (ESN) based prediction algorithm is proposed for predicting the future positions of users based on the real dataset. Thirdly, a proposed multi-agent Q-learning based algorithm is invoked for predicting the position of UAVs in each time slot based on the movement of users. The algorithm is proved to be able to converge to an optimal state equation. Numerical results are provided to demonstrate that as the size of the reservoir pool increases, the proposed ESN approach improves the prediction accuracy. Finally, we demonstrate that throughput gains of about 17% are achieved.