# Research on Satellite Network Traffic Prediction Based on Improved GRU Neural Network
###### Abstract  
The current satellite network traffic forecasting methods cannot fully exploit the long correlation between satellite traffic sequences, which leads to large network traffic forecasting errors and low forecasting accuracy. To solve these problems, we propose a satellite network traffic forecasting method with an improved gate recurrent unit (GRU). This method combines the attention mechanism with GRU neural network, fully mines the characteristics of self-similarity and long correlation among traffic data sequences, pays attention to the importance of traffic data and hidden state, learns the time-dependent characteristics of input sequences, and mines the interdependent characteristics of data sequences to improve the prediction accuracy. Particle Swarm Optimization (PSO) algorithm is used to obtain the best network model Hyperparameter and improve the prediction efficiency. Simulation results show that the proposed method has the best fitting effect with real traffic data, and the errors are reduced by 26.9%, 37.2%, and 57.8% compared with the GRU, Support Vector Machine (SVM), and Fractional Autoregressive Integration Moving Average (FARIMA) models, respectively. satellite network; flow forecast; attention mechanism; neural network +
Footnote †: journal: 10  
be congested and unable to respond to the service in real time. Through the forecast of satellite network traffic, we can grasp the changing characteristics and trends of network traffic in advance. At the same time, according to the traffic load of each satellite, the path selection can be planned to avoid local congestion in the network and improve the routing strategy efficiency of the network. Aiming at the problems of low prediction accuracy and low efficiency of current satellite network traffic prediction methods, this paper fully considers the characteristics of self-similarity and long correlation of satellite network traffic data and proposes an improved GRU neural network satellite network traffic prediction model. Based on the traditional GRU neural network, this paper integrates the attention mechanism to form a new neural network model, which pays attention to the importance of traffic data and hidden state. It not only learns the time-dependent characteristics of input sequences but also mines the interdependent characteristics of data sequences, thus improving the accuracy of traffic prediction. At the same time, the particle swarm optimization (PSO) algorithm is used to determine the optimal super-parameter combination of the model, which makes the model have higher prediction efficiency in traffic forecasting. The contributions of this paper are as follows:  
1. In this paper, the correlation characteristics of satellite network traffic are fully considered, and the nonlinear time dynamic correlation is obtained by using a gating unit to avoid gradient disappearance or gradient explosion during training. 2. In the coding and decoding stages of the GRU network, an attention mechanism is introduced, and multiple intermediate vectors are added to uniformly process the time series and input information of the intermediate vectors at the current movement. 3. Particle swarm optimization algorithm is used to adjust the hyperparameters of the neural network. The organizational structure of this paper is as follows: The first part introduces the current research status of traffic forecasting at home and abroad. The second part introduces the definition of the satellite network traffic prediction problem and the overall framework of the Attention-GRU (AT-GRU) satellite network traffic prediction model proposed in this paper. The third part introduces the realization principle of each part of the model, explains the coding and decoding unit design module of the new prediction model in detail, and introduces the determination method of the new model parameters and the loss function. In the fourth part, the simulation comparison is carried out.