# Research on Satellite Network Traffic Prediction Based on Improved GRU Neural Network
## 3 Definition and Model of the Satellite Traffic Forecast Problem  
Figure 1 shows a system structure diagram of satellite communication. A satellite communication system consists of the space-based network, adjacent space and the ground-based network. The space-based network consists of Geosynchronous Earth Orbit (GEO), Middle Earth Orbit (MEO), and Low Earth Orbit (LEO), which mainly realize global coverage and broadband access. Adjacent space is composed of some aircraft, which mainly realize the function of edge service. The ground-based network is composed of ground gateways, network control centers, and satellite control centers, and is mainly  
responsible for network services in business-intensive areas. The terminal is a device that inputs programs and data to the computer or receives the processing results output from the computer via communication facilities. Satellite terminals can be divided into handheld terminals, portable terminals, vehicle-mounted terminals, etc., according to terminal types. Each satellite terminal is responsible for managing terminal connections in multiple systems. The terminal will be connected to the default gateway station of its beam when accessing the satellite network for communication. In the satellite uplink, the traffic generated on the ground is uploaded to the satellite, which then forwards it to the ground gateway station. In the satellite downlink, ground gateway stations and data centers complete traffic monitoring [17]. The traffic of all satellites comes partly from other satellites and partly from the ground network. Network traffic prediction can guarantee high-quality communication, so it is widely used in many satellite applications. Satellite traffic has complex characteristics such as self-similarity and long correlation. Different from the terrestrial network, the available resources of the satellite network are more limited, and the topological structure of the satellite network changes from time to time. The satellite traffic prediction algorithm must take into account both accuracy and efficiency. Most ground network prediction models have high computational complexity. If the ground network traffic prediction model is directly applied to the satellite, it will increase the burden on the satellite. In this paper, a new neural network satellite network traffic prediction model with an attention mechanism is proposed, which can be applied to satellite network traffic prediction in terms of prediction accuracy and operation efficiency. The flow forecast model diagram is shown in Figure 2. The flow forecast mathematics is set as follows:  
Figure 1: Satellite communication model diagram. Given satellite network traffic data \(Y\), where \(Y=(y_{1},y_{2},\ldots,y_{T})\) represents the target sequence within the length of the window \(T\). Based on the given time series information, the goal of satellite network traffic prediction is to predict the value of the time series \(Y\) before the \(t\) timestep, which can be expressed as follows  
\[\hat{y}_{T+t}=F(y_{1},y_{2},\ldots,y_{T}), \tag{1}\]  
where \(\hat{y}_{T+t}\) represents the predicted value and \(F(\cdot)\) represents the nonlinear mapping function. The overall structure model of this paper is shown in Figure 2. Based on the traditional GRU coding and decoding structure, this paper integrates the attention mechanism. The traditional coding-decoding structure must compress all the input information into fixed-length vectors. Using this simple fixed-length coding to represent longer and more complex inputs often leads to the loss of input information. The attention model allows the decoder to access the output generated by all encoders to overcome the above shortcomings. Its core idea is that all the outputs of the encoder are weighted and combined, and then input into the decoder at the current position to influence the output of the decoder.