Joint Trajectory Design and BS Association for Cellular-Connected UAV: An Imitation-Augmented Deep Reinforcement Learning Approach - Chen, Yu-Jia; Huang, Da-Yu (IEEE internet of things journal, 2022)
This article concerns the problem of the trajectory design and base station (BS) association for cellular-connected unmanned aerial vehicles (UAVs). To support safety-critical functions, one primary requirement for UAVs is to maintain reliable cellular connectivity at every time instant during the flight mission. Since the antenna gain of a ground BS (GBS) changes with the position of the UAV, the UAV-GBS association strategy should be jointly considered with the trajectory design, which has not been studied in the prior arts. In this article, we first formulate the problem of joint BS association and trajectory design with the objective of minimizing the mission completion time under a connectivity outage constraint. Then, a deep learning framework is proposed to solve the formulated nonconvex optimization problem in a decoupled manner. For the UAV-GBS association strategy, the signal strength radio map of a given area is constructed, which is used to train a deep neural network (DNN) to approximate the nonlinear mapping from the UAV position to the optimal GBS. To tackle the high complexity due to the coupled decision variables of GBS association and UAV movement, a novel deep reinforcement learning (DRL) approach is developed to learn the optimal trajectory, in which the UAV can learn from its own past good experiences. Our simulation results confirm the superiority of the proposed DRL approach compared to the conventional DRL approaches in terms of the trajectory length. Additionally, it is demonstrated that the nearest association scheme fails to provide reliable cellular connections, whereas our proposed approach can ensure strong connectivity with the GBS during the whole trajectory.