Research on Mobile Communication Network Traffic Prediction Method Based on Deep Learning
In view of the problem of high RMSE and MAE due to mobile communication net-work traffic redundancy prediction,the paper proposes a mobile communication network traffic prediction method based on deep learning.The method regards the network traffic as a time se-ries,analyzes the self-similarity of the network traffic composition structure,defines the net-work traffic features on the graph structure,and extracts the traffic self-similarity features.The deep learning algorithm is introduced,the graph signal matrix represents the predicted node characteristics,and the multi-layer neural network is used to realize the convolution operation and construct the traffic prediction algorithm.Spatially hidden features with attention informa-tion are updated to realize the optimization of mobile communication network traffic prediction.The experimental results show that the RMSE of the method is in the range of 0.000～0.009 and the MAE is in the range of 0.000～0.07010-3,and the prediction error is 0 when using H_9 function,indicating that the method has high prediction accuracy and good prediction performance.
deep learningmobile communication networknetwork trafficprediction method
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