Computing Power Allocation Strategy Based on Diffusion Model in Satellite Edge Networks

Via coordinated management of ground networks, satellite networks, and near-earth unmanned aerial vehicle networks, computing-empowered space-air-ground integrated networks can achieve global connectivity and universal intelligence, providing strong support for the development of China's digital economy. Low Earth Orbit (LEO) satellites have the ability of ubiquitous connectivity and edge computing, which provides the basis for an efficient computing system for space-air-ground integration. By synchronizing the Mobile Edge Computing (MEC) to LEO satellite networks to form a service-oriented end-edge-cloud three-level computing architecture, latency-sensitive tasks can be offloaded from terminals to LEO satellites, which improves the task completion rate. However, methods to make efficient offloading decisions and compute power allocation in LEO satellite edge networks must be developed urgently. Aiming at high dynamics in the satellite network environment and the discrete-continuous hybrid action space, this study proposes a Hybrid Proximal Policy Optimization (H-PPO) method based on the generative diffusion model. First, a wireless channel with time-varying characteristics is modeled and service latency, communication, and computation models under different offloading decisions are constructed. Second, under the multiple constraints of offloading decisions, remaining computing resources, and power control, a long-term optimization problem for maximizing the average task completion rate is constructed. Subsequently, the Markov decision process with parameterized actions is established and the generative diffusion model is introduced as the discrete action policy to improve the sampling efficiency and exploration ability of traditional Deep Reinforcement Learning (DRL) methods. Finally, the proposed method is used to jointly optimize the computing offloading, computing power allocation, and power control. The simulation results show that the proposed method has a better convergence performance and is superior to the three comparison methods in terms of task completion rate.