Cellular Network Traffic Prediction Based on Spatio-temporal Graph Network with Multiple Temporal Granularity
Cellular network traffic prediction is of great significance for operators to improve network service quality,reduce energy con-sumption,and optimize resource allocation.A cellular network traffic forecasting approach based on a multi-time granularity spatio-temporal graph neural network is proposed to address the problem that current cellular network traffic forecasting methods cannot extract multi-time granularity sequence features and spatial features effectively.The historical traffic data of the base station is modeled as time series of multiple time granularities,and the one-dimensional convolutional network is applied to extract the features of each sequence.Then the graph attention network is employed to aggregate the features of multi-time granularities to obtain the embedding of a single base station.Finally,the embeddings of multiple base stations are spatially aggregated,and the final prediction result for each base station is obtained via a fully connected network.The public dataset Telecom Italia is used to verify the effectiveness of the proposed method,and RMSE and R2 are used as evaluation indicators for the prediction results.The proposed method can achieve the best prediction results compared with the current existing methods.Finally we analyze the influence of different time granularity sequences on the final prediction results.The results show that sequences with time granularity between 40 minutes and 1.5 hours make the greatest contribution to improving the model prediction effect.
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