Computation-Offloading Optimization for Satellite Edge Computing via Diffusion and Lyapunov-Based Deep Reinforcement Learning

Satellite edge computing (SEC) extends the capabilities of edge computing technology to satellite networks, facilitating rapid local processing of global task requirements. Deep reinforcement learning (DRL) has emerged as a promising approach for SEC scenarios due to its inherent dynamic adaptability, complex state modeling capability, and long-term optimization potential. However, existing DRL-based computing offloading techniques continue to encounter challenges, including low sample efficiency, poor decision quality, and insufficient long-term stability, which constrain their performance in real satellite network environments. To address these challenges, this study proposes a diffusion and DRL-based approach for computation offloading in SEC networks called the generative artificial intelligence-DRL. First, by implementing the cooperative computing model of the multiSEC, this study comprehensively considers the heterogeneous computing and communication capabilities of satellite nodes, diversity of task types, and dynamic distribution of resources in an offloading strategy, thereby ensuring long-term system sustainability under dynamic resource constraints and provides a solid foundation for computation offloading in satellite networks with time-varying resource. Second, we integrate generative diffusion modeling into the DRL framework to enhance policy generation by producing contextually relevant and high-quality action samples. This not only reduces the dependence on large-scale training data but also improves decision precision and generalization in complex, high-dimensional environments. Finally, a Lyapunov optimization framework is introduced to transform the offloading problem into an online per-slot optimization process, thereby ensuring the long-term stability of the SEC system under dynamic and unpredictable task arrivals and environmental conditions. The experimental results demonstrate that the method proposed offers significant advantages over the existing approaches in reducing task latency and enhancing system stability.