电讯技术2025，Vol.65Issue(6)：903-912,10.DOI:10.20079/j.issn.1001-893x.241230005
面向公平性的无人机协同轨迹优化与任务卸载
Cooperative Trajectory Optimization and Task Offloading of UAVs for Fairness
摘要
Abstract
In order to meet the computing needs of user devices in Internet of Things(IoT)systems,unmanned aerial vehicles(UAVs)carrying mobile edge computing servers are used to collaborate with user devices to offload tasks.In order to solve the data privacy and data overhead problems existing in user devices,federated learning is introduced for model training.In view of the irrational allocation of computing resources,the user fairness is considered,and the fairness factor is introduced based on the Jain fairness index.By jointly optimizing the flight trajectory and unloading decisions of each UAV,the total energy consumption of the system and the user fairness in the coverage area are jointly maximized,and a Federated Reinforcement Learning combined with Actor-Critic Network(FRLACN)is proposed.The algorithm uses Actor-Critic to generate the optimal decision action for each device,and performs a more accurate gradient update to make full use of its heterogeneous resources.The simulation results show that the proposed FRLACN algorithm reduces the total energy consumption by 11%and the data transmission cost by 8.7%compared with the traditional federated learning algorithm,and improves the user fairness.关键词
多无人机协同/任务卸载/移动边缘计算/联邦强化学习/公平性Key words
multi-UAV collaboration/task offloading/mobile edge computing/federal reinforcement learning/fairness分类
电子信息工程引用本文复制引用
田旭,王华华,廖福建,郑少杰..面向公平性的无人机协同轨迹优化与任务卸载[J].电讯技术,2025,65(6):903-912,10.基金项目
重庆市自然科学基金创新发展联合基金(中国星网)(CSTB2023NSCQ-LZX0114) （中国星网）
