Low-complexity Cellular Traffic Prediction Algorithm Based on Lightweight Convolutional Neural Network
With the rapid growth of data traffic demand in cellular networks,accurate prediction of cellular traffic conditions at fu-ture moments can improve network resource allocation,achieve traffic load balancing,and deploy base station energy saving and sleeping strategies.Based on the lightweight linear bottleneck module,a spatiotemporal prediction model with multiple parallel branching modules is proposed to extract spatiotemporal features from recent historical data and periodic historical data,respectively.Meanwhile,for the spatial dependency in the gridded spatiotemporal data,high-dimensional grid features are additionally clustered by K-Means algo-rithm,and the grid base station density information is extracted and fed into the model as a cross-domain feature,thus realising accurate prediction of the cellular traffic in the whole area of the study range deploying a low-complexity and low-computing-power-demand model.
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