Feiyue Zhu
ABSTRACT
Due to the response to the topological time-varying of satellite network, the satellite management system puts forward higher requirements for the network traffic prediction algorithm. The traffic prediction algorithm of ground network is not suitable for satellite network. In this manuscript, a neural network model of long and short-term memory with attention mechanism is proposed. Considering that the input and output of traffic prediction is a sequence, the long short-term Memory (LSTM) model in this manuscript balances the effects of different parts of input on output by adding attention mechanism. The simulation results show that compared with ARIMA, random forest and traditional Recurrent Neural Network (RNN), the prediction accuracy of this model is significantly improved. Meanwhile, compared with the model after removing the attention network, the model verifies the effectiveness of the attention network.
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Index Terms
- An LSTM-based Traffic Prediction Algorithm with Attention Mechanism for Satellite Network
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