面向无人机低时延任务的通感算资源协同调度方法OA

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.