Trajectory Optimization for Age of Information Minimization in UAV Communication Systems

Baolin Yin, Xinmin Li, Xiaoqiang Zhang, Lili Wei

Abstract: Unmanned aerial vehicle (UAV) as a promising technology in the 6G communication system can collect and transmit information intelligently. However, the existing methods are difficult to design UAV's trajectory to guarantee the information freshness performance. In this paper, the information freshness of a multiple-UAV communication system is modeled based on the age of information (AoI) and the minimization AoI optimization problem subjected to the minimal energy is formulated. In order to solve this nonconvex optimization problem, reinforcement learning (RL)-based scheme is proposed to design the UAVs' trajectory. The proposed scheme constructs the reward function depending on the accumulated AoI to make a fast trajectory decision and reduce the AoI of UAV communication system. The simulation results show that the proposed scheme can improve 21.7% performance gain of information freshness compared to the random scheme and the greedy scheme, and 7.7% performance gain compared to the flying-hover-communication scheme. In addition, the proposed UAV trajectory design scheme has the superior convergence.