# Predator-Prey Model Based Asymmetry Resource Allocation in Satellite-Terrestrial Network
## 2 Related Works  
The existing research on load balancing in the communication network mainly involves two aspects: terrestrial cellular network and satellite communication network. In the terrestrial cellular network, considering the inter-cell interference problem, the authors proposed a mobile load balancing algorithm compatible with ICIC method in reference [7], and the spectrum utilization is significantly improved. Considering the quality of service and resource consumption, the authors proposed a distributed algorithm in reference [8], which considers the neighborhood unit and non-neighborhood unit to achieve optimization. In reference [9], based on the traditional MLB (mobility load balancing), the authors proposed a load allocation strategy and a target selection strategy, which realized the matching by judging the priority level, and handed over the users of the overloaded cells to the adjacent service cells. In reference [10], the authors proposed a mobile load balancing algorithm based on utility and the concept of load balancing efficiency factor, which improves the sorting efficiency and load balancing efficiency of overload cells in MLB algorithm. For the failure of load balancing algorithm caused by the large difference of traffic distribution in cells, the authors proposed a multi-service load balancing algorithm in reference [11], which is proven to be an effective dynamic switch algorithm for load balancing. In reference [12], in the research of an adaptive algorithm for mobile load balancing in a cellular network, based on the network load, the overload cells are determined by an adaptive threshold to achieve load balancing. Based on the limitations in reference [12], the authors proposed a load balancing strategy in a heterogeneous network based on clusters in reference [13], which realized the load balancing optimization within the cluster and effectively improved the load balancing efficiency and throughput. In our opinion, most of the above algorithms only start the load balancing algorithm when congestion occurs, which is a static process and is not timely enough to solve the load unbalancing problem. Considering the satellite network in reference [14], the authors proposed a dynamic adaptive strategy, which considered data transmission delay and link weight to achieve dynamic routing selection and load balancing. In reference [15], the authors proposed an adaptive load balancing algorithm based on the response requirements of burst hot traffic, which achieves load balancing by updating network load periodically based on congestion matrix. In reference [16], the concept of on-demand routing based on satellite topology was introduced for the first time, and a location-based on-demand routing protocol was proposed to realize load balancing. In reference [17], the authors proposed a distributed routing scheme, which combined the communication cost of inter satellite link and the delay of information transmission queue to realize the dynamic update of routing and effectively reduce the end-to-end delay and packet loss rate. Based on this scheme, the authors considered multiple factors to optimize the agent-based routing strategy in reference [18], which is based on the on-demand allocation and priority policy of the implementation of QoS routing. In reference [19], the authors proposed a distributed load balancing strategy based on the concept of virtual node routing, which realizes dynamic mapping through logical address. In reference [20], the authors considered the congestion problem caused by local deviation in LEO satellite network, and proposed the resource allocation between neighboring satellites, that is, to realize the flow of redundant bandwidth resources and realize the balance of traffic resources in the domain. In reference [21], the authors proposed that nodes in satellite networks store congestion information of global nodes, to realize task allocation among nodes, and to reduce the task load of high load nodes.