Computation Offloading and Resource Allocation Strategy Considering User Mobility in Multi-UAV Assisted Semantic Communication Networks

Multi-unmanned aerial vehicle (UAV)-assisted communication is a critical technology for the low-altitude economy, supporting applications from logistics to emergency response. Semantic communication effectively enhances transmission efficiency and improves the communication performance of multi-UAV-assisted systems. Existing research on multi-UAV semantic communication networks predominantly assumes static ground devices, overlooking computation offloading and resource allocation challenges when ground devices are mobile. This overlooks the critical challenge of dynamically managing computation offloading and resources for mobile users, whose varying channel conditions and semantic compression needs directly impact system performance. To address this gap, this paper proposes a multi-UAV-assisted semantic communication model that novelly integrates user mobility with adaptive semantic compression, formulating a joint optimization problem for computation offloading and resource allocation. The objective is to minimize the maximum task processing latency through the joint optimization of UAV–device association, UAV trajectories, transmission power, task offloading ratios, and semantic compression depth. To solve this problem, we design a MAPPO-APSO algorithm integrating alternating iteration, multi-agent proximal policy optimization (MAPPO), and adaptive particle swarm optimization (APSO). Simulation results demonstrate that the proposed algorithm reduces the maximum task latency and system energy consumption by up to 20.7% and 16.1%, respectively, while maintaining transmission performance and outperforming benchmark approaches.