# Predator-Prey Model Based Asymmetry Resource Allocation in Satellite-Terrestrial Network
## 1 Introduction  
With the development of Internet technology and the vigorous development of logistics and transportation, material exploration, and digital city, higher data communication requirements and more accurate location information have become the main characteristics of the next generation information age [1]. Relying only on a ground mobile network cannot bear the huge impact of data volume. Considering the rapid change of ground terminal facilities and satellite networking in recent years, with the rapid development of technology, satellite network has greater advantages in round-trip delay and coverage performance, which can provide a wide range of coverage area and remote communication [2]. Therefore, the joint construction of satellite ground deep integration networks by ground mobile communication networks with large transmission capacity is an efficient solution for this scenario, and it is an important direction for the development of 6G communication in the future. Through the increasingly perfect ground network and wide coverage area of a space-based network, the idea of a 6G ubiquitous connection and holographic link can be effectively realized [3].  
However, the integration of a cellular mobile network and satellite communication network is facing serious challenges. As for a ground cellular network, with the coverage of a 5G network and the improvement of mobile terminal mobility, there is a serious imbalance of resource allocations in small-scale cellular networks [4]. In the satellite communication network scenario, a complex deep space environment and satellite network topology will lead to a certain end-to-end communication delay. In addition, considering the regional problem of coverage density in the existing satellite network and the actual regionalcommunication demand and the service allocation problem, there is an uneven distribution of satellite network traffic [5]. These factors will lead to network congestion in local areas of the satellite network, while the network resource in some areas are idle.  
For the above problems, the traditional communication network routing algorithm is no longer applicable, and we need to consider reducing the number of congested links through a load balancing algorithm to achieve effective network load balancing [6]. In the communication network, load balancing technology is used to transfer the overloaded node services to the underloaded node when the network congestion occurs in order to realize the balanced distribution of services. The traditional load balancing routing algorithm is a reactive trigger, i.e., load balancing is carried out only when congestion occurs to save processing resources. However, in the face of current huge data communication and efficient business processing needs, it cannot provide effective services support. This paper constructs a new resource allocation model to achieve load balancing based on the predator-prey model for a 6G communication scenario, where the relationships among the satellites are asymmetry relations. The load balancing algorithm will keep working to stabilize the whole network service, which means that the proposed algorithm will be more practical in data processing efficiency and data transmission rates. The main contributions are as follows:  
* We divide the satellites in a 6G communication scenario into two groups, and we formulate the relations between these two group satellites as a predator-prey model.
* We use the Lotka-Volterra-based predator-prey model to analyze the problem among these two groups, which is an asymmetry resource allocation problem.
* We prove the correctness and effectiveness of the proposed model.  
The remainder of the paper is organized as follows. Section 2 discusses the related works on load balancing in a satellite network. Section 3 introduces the system model and problem formulation. Section 4 provides the analysis for the proposed predator-prey model for load balancing. Numerical simulations are given in Section 5. Finally, the conclusion is provided in Section 6.