  For UAV-aided wireless systems, online path planning attracts much attention
recently. To better adapt to the real-time dynamic environment, we, for the
first time, propose a Monte Carlo Tree Search (MCTS)-based path planning
scheme. In details, we consider a single UAV acts as a mobile server to provide
computation tasks offloading services for a set of mobile users on the ground,
where the movement of ground users follows a Random Way Point model. Our model
aims at maximizing the average throughput under energy consumption and user
fairness constraints, and the proposed timesaving MCTS algorithm can further
improve the performance. Simulation results show that the proposed algorithm
achieves a larger average throughput and a faster convergence performance
compared with the baseline algorithms of Q-learning and Deep Q-Network.
