Satellite-Aided Low-Altitude UAV Service Migration With Semantic Extraction and Generated Graphs

Qiongqiong Jia, Jie Zheng, Ling Gao, Jinping Niu, Rui Cao, Jie Ren

Abstract: Satellite-aided low-altitude UAV networks utilize satellites to support low-flying drones with communication and navigation for tasks like sensing, surveillance, and delivery. A critical challenge is offloading UAV communication traffic and migrate services to satellites during network congestion. The decentralized structure, changing link conditions, and limited local visibility make it hard to coordinate service migration and request routing in such dynamic environments. To address these challenges, we propose a semantic graph-based multi-agent reinforcement learning (MARL) framework for satellite-aided UAV networks. We formulate service migration and routing as a semantic graph optimization problem, with the objectives of reducing communication delay and increasing network throughput. The framework incorporates two key components: a cyclic message-passing model that enables nodes to infer global network states from limited local observations, and a discrete denoising diffusion model that generates realistic, and dynamic topologies. Our framework leverages semantic feature extraction to further enhance decision-making in routing and service placement. Extensive simulations show that our approach achieves significant reductions in average transmission delay and improvements in the network throughput.

External IDs:doi:10.1109/tccn.2025.3645425