## 2 Related Work  
### Satellite Network Resource Allocation  
Satellite network resource allocation is the reasonable allocation of limited spectrum, power, time, and other resources in satellite communication systems. The main goal of resource allocation is to optimize network performance, increase system capacity, reduce communication delay, and ensure that each user can obtain a QoS that meets their communication requirements. Among the existing satellite network resource allocation algorithms, we have the research by [PERSON] et al. [1], who built a Low Earth Orbit (LEO) satellite communication system based on beam hopping and adopted a greedy algorithm to solve the resource allocation problem. Beam hopping is a technique used in satellite communication to manage and optimize signal coverage and resource allocation. Under this mechanism, the communication beam of the satellite quickly switches between different regions to adapt to the needs and communication loads of different areas and this system can flexibly allocate satellite resources according to changing business needs. Regarding the load balancing issue of cache resource allocation, [PERSON] et al. [2] proposed a cache resource allocation method based on Stackelberg game. By intelligently caching content and allocating resources in satellite networks, their goal is to reduce latency, improve data transmission efficiency, and achieve network load balancing, and the proposed method can optimize the cache resource allocation problem in Geostationary Earth Orbit (GEO) satellites. Although these aforementioned methods account for the distinctive characteristics of satellite networks, they have not yet attained optimal performance in resource allocation algorithms. Satellite networks combined with edge computing have also achieved some results in resource allocation. For example, [PERSON] et al. [3] modeled the unloading decision problem as a bilateral matching game problem involving LEO and GEO satellites, and proposed an unloading decision scheme based on an improved bilateral many to one matching game algorithm. This algorithm mainly considered the improvement of delay and energy cost, and can achieve the optimization of computing resource and radio resource allocation. [PERSON] et al. [4] decomposed the increasing computing demand problem into two subproblems: computing offloading and resource allocation. They proposed a joint computation offloading and resource allocation (JCORA) strategy and the Lagrange multiplier method was used to realize computing resource allocation. The results showed that the JCORA strategy can effectively reduce system costs. Due to the inherent complexity of satellite network environments and the limitations of available resources, a significant body of research focused on developing resource allocation strategies. [PERSON] et al. [5] proposed a task-aware contact plan design method aimed at optimizing resource allocation and data transmission efficiency in small satellite networks. The study considered the unique characteristics of satellite networks, such as the dynamic nature of satellites, limited resources, and the diversity of tasks, and emphasized the importance of accounting for channel conditions and network density when designing satellite network tasks and data transmission strategies. [PERSON] et al. [6] introduced a collaborative data download method utilizing inter-satellite links to improve the data transmission efficiency of LEO satellite networks. The research examined how to enhance satellite networks performance and data transmission efficiency under constrained resource conditions through intelligent scheduling and the use of inter-satellite links. [PERSON] et al. [7] presented an ultra-dense LEO network architecture to improve data transmission efficiency by integrating space and ground networks.