# Matching-Based Resource Allocation for Satellite-Ground Network
## 2 Related Work  
Recently, to enable seamless mobile communications coverage worldwide, the satellite-ground network attracted tremendous research interest. Reference [15] developed an air-ground integrated network composed of satellites, UAVs, and ground users, which provided users with lower transmission delay. Reference [16] proposed a software-defined integrated space-ground network, which provided integrated communication services for various space missions. Reference [17] proposed a satellite-ground network composed of a ground network and a space network to provide users with global network coverage and less delay. Reference [18] proposed cognitive radio, which allowed satellite and ground networks to dynamically share their spectrum, so that the network obtained greater throughput. Reference [19] studied the connection delay between satellite networks in different long-distance intercontinental data communication scenarios, and proved the availability of satellite networks. It can be seen that the efficient transmission of large amounts of data and the limits of network coverage can now be addressed by the integrated satellite-ground network.  
Resource allocation is an important research direction to improve the transmission performance of the satellite--ground network [20]. Paper [21] established a joint communication and computation optimization model for satellite networks, which utilized stochastic geometry and queuing theory to achieve the best response delay. Reference [22] designed a dynamic channel resource allocation algorithm for the satellite ground integration architecture, which improved the number of Internet of Things terminal connections. The literature in [23] proposed a joint beamforming and carrier allocation scheme to solve the problem of spectrum shortage, which realized reliable satellite ground resource allocation. Reference [24] studied the problem of maximizing the capacity of all mobile terminals in an air-ground integrated network, and mainly used convex optimization theory to solve the problem. Reference [25] introduced some challenges faced by satellite-ground communication networks and proposed solutions based on artificial intelligence. The mentioned paper only wanted to improve the date rate of resources and the throughput of the system, and did not allocate resources from the needs of various entities in the network.  
Matching is widely used in resource allocation [26]. The method of using matching theory to obtain optimal resource allocation in network resource allocation has become widespread. Two-sided matching has been widely studied in network resource allocation. Reference [27] designed an algorithm based on one-to-many matching, which transformed the network optimization problem into a one-to-many matching problem between spectrum resources and end users. Reference [28] proposed a matching-based theoretical framework to solve the issue of distributing computing resources in a three-layer fog computing network. The above papers only consider the case of two types of entities. However, in the satellite-ground network resource allocation, three different categories of entities must be taken into account equally. In social and commercial domains, three-sided matching problems are particularly prevalent [29; 30]. Reference [31] utilized a three-sided matching algorithm to find the optimal matching between space-air-ground network facilities, content sources, and users. Paper [32] studied the problem of three-sided matching between spectrum, equipment, and user in wireless network virtualization. The above three-sided allocation method provides guidance for our study. However, as far as the author knows, the three-sided matching game is used for the first time in this research to address the issue of multi-party entity matching in the satellite-ground network.