Collaborative Computation and Migration in Multi-UAV Networks With Semantic Communication

Multi-Uncrewed Aerial Vehicle (UAV)-assisted Mobile Edge Computing (MEC) is a key technology for future 6G networks, providing wide coverage and flexible computing services. However, the limited resources of UAVs and the dynamic changes in the network structure make it difficult to maintain high efficiency. Existing methods often ignore the semantic information of tasks and the complex relationships among UAVs and mobile terminals, resulting in poor coordination. This paper proposes a joint optimization framework for task offloading, task migration, and trajectory control in semantic communication-enabled multi-UAV edge computing systems, aiming to maximize semantic similarity while minimizing task latency and system energy consumption. To tackle the resultant problem, we develop a Heterogeneous Graph Attention Network-based Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (HAN-MATD3) algorithm. Specifically, we use a heterogeneous graph to model the network topology and apply HAN to extract important semantic features. These features are then integrated into the MATD3 framework to support joint optimization. Simulation results show that HAN-MATD3 improves the converged reward by 5.3% over MATD3 (and 17.7% over MADDPG), and reduces average latency by up to 12.2%-14.6% under the bandwidth and CPU settings, while maintaining higher semantic similarity.