移动通信2026，Vol.50Issue(4)：107-116,10.DOI:10.3969/j.issn.1006-1010.20260128-0001
面向无人机低时延任务的通感算资源协同调度方法
Collaborative Scheduling of Integrated Sensing,Computing,and Communication Resources for Low-Latency UAV Tasks
摘要
Abstract
To address low-latency tasks such as high-definition video transmission by unmanned aerial vehicles(UAVs)in emergency communication scenarios,integrated sensing and communication technology can utilize environmental sensing information to assist millimeter-wave beam tracking,thereby improving the stability and transmission efficiency of air-to-ground communication links.However,at the base station side,fierce competition for computing resources exists between high-frequency radar sensing tasks and computation offloading tasks.To address the challenge,this paper constructs a joint optimization framework encompassing edge server resource allocation,task queue scheduling,and UAV trajectory planning.First,the computational resource preemption cost of the sensing process is explicitly characterized,and a priority-based queuing scheduling along with timeout dropping mechanism is designed for mixed low-latency task flows.Second,multi-UAV trajectory planning,task offloading decisions,and computing power allocation ratios are uniformly modeled as a Markov decision process,and a proximal policy optimization algorithm based on a potential-guided reward is proposed.This algorithm adopts a centralized actor-critic architecture to evaluate the global state value and innovatively introduces an artificial potential field-guided reward,effectively overcoming the challenges of sparse rewards and position oscillation in high-dimensional continuous action spaces during long-duration trajectory optimization.Simulation results demonstrate that the proposed method effectively reduces the average computation latency of UAV tasks while guaranteeing sensing accuracy and task completion rates,validating the effectiveness of the collaborative communication-sensing-computing resource scheduling mechanism in UAV low-latency task scenarios.关键词
通感一体化/移动边缘计算/无人机通信/深度强化学习/轨迹优化Key words
integrated sensing and communication/mobile edge computing/UAV communications/deep reinforcement learning/trajectory optimization分类
信息技术与安全科学引用本文复制引用
张璇,杨志祥,周凡钦,李文璟..面向无人机低时延任务的通感算资源协同调度方法[J].移动通信,2026,50(4):107-116,10.基金项目
国家自然科学基金面上项目"去蜂窝空中基站网络的组网与传输关键技术"(62471057) （62471057）
中国博士后科学基金项目"STAR-RIS辅助的蜂窝无源物联网高能效通信机制"(GZC20252311) （GZC20252311）
中国博士后科学基金项目"STAR-RIS赋能的蜂窝无源物联网通信全链路优化方法"(2025M773506) （2025M773506）
