Uplink Throughput Maximization in UAV-Aided Mobile Networks: A DQN-Based Trajectory Planning Method

Abstract

This paper focuses on the unmanned aerial vehicles (UAVs)-aided mobile networks, where multiple ground mobile users (GMUs) desire to upload data to a UAV. In order to maximize the total amount of data that can be uploaded, we formulate an optimization problem to maximize the uplink throughput by optimizing the UAV’s trajectory, under the constraints of the available energy of the UAV and the quality of service (QoS) of GMUs. To solve the non-convex problem, we propose a deep Q-network (DQN)-based method, in which we employ the iterative updating process and the Experience Relay (ER) method to reduce the negative effects sequence correlation on the training results, and the ε-greedy method is applied to balance the exploration and exploitation, for achieving the better estimations of the environment and also taking better actions. Different from previous works, the mobility of the GMUs is taken into account in this work, which is more general and closer to practice. Simulation results show that the proposed DQN-based method outperforms a traditional Q-Learning-based one in terms of both convergence and network throughput. Moreover, the larger battery capacity the UAV has, the higher uplink throughput can be achieved.

Keywords

• aerial base station (ABS)
• deep reinforcement learning
• Q-learning
• trajectory optimization