Computing Resource Allocation in LEO Satellites System: A Stackelberg Game Approach

Past few years have witnessed the compelling applications of the remote sensing satellites in our daily life, ranging from the weather forecast to military surveillance. Due to the computation and power constraints, the LEO satellites have to download the remote sensing data to the ground stations for further processing. However, the long-distance transmission and the ionospheric interference is problematic for supporting the latency-sensitive remote sensing services, such as hotspot detection, hotspot tracing. As a remedy, in this paper, the space stations are introduced to offload the computation task of the remote sensing satellites to reduce the transmission delay. With the space station joining in, a three-tier intelligent remote sensing satellites operation system is constructed. In order to perform well, we study the computation resource allocation strategies in this three-tier system. We model the resource management and pricing problems among three players as a Stackelberg game, where the space stations act as the leaders, the ground stations as the followers, and the LEO satellites as the sub-followers. For searching the Nash equilibrium of this game, we apply ’WoLF-PHC’ algorithm for learning the optimal resource management strategies. In addition, some simulation results are presented to demonstrate the feasibility and performance of our architecture and algorithm.