Reinforcement Learning-Based Age of Information Optimization in UAV-Enabled Communication System

被引：0
|
作者：
Li X. [1 ]
Yin B. [1 ]
Wei L. [1 ]
Zhang X. [1 ]
机构：
[1] School of Information Engineering, Southwest University of Science and Technology, Mianyang
来源：
Dianzi Keji Daxue Xuebao/Journal of the University of Electronic Science and Technology of China | 2022年 / 51卷 / 02期
关键词：
Age of information; Reinforcement learning; Trajectory optimization; Unmanned aerial vehicles; Wireless telecommunication system;
D O I：
10.12178/1001-0548.2021128
中图分类号：
学科分类号：
摘要：
Aiming at solving the characterization and optimization of information freshness in the sixth generation (6G) communication system, we firstly model information freshness based on the age of information (AoI) in the unmanned aerial vehicle (UAV) communication system and formulate an AoI minimization problem subjected to the energy consumption. However, the nonconvex problem is difficult to solve due to discreteness of AoI optimization and the complicated energy consumption expression. A reinforcement learning-based scheme is proposed to design the UAV's trajectory, in which the reward function related to AoI is constructed to realize a fast and intelligent UAV trajectory decision, thus reducing the AoI of UAV communication system. The simulation results show that, compared with the benchmark schemes, the proposed trajectory design scheme can improve the information freshness by 8.51%~21.82%. In addition, the proposed scheme has a superior convergence. Copyright ©2022 Journal of University of Electronic Science and Technology of China. All rights reserved.
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页码：213 / 218
页数：5
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