Mobile cellular traffic efficient prediction method based on space-time aggregation graph convolutional network

Abstract

The invention discloses a mobile cellular traffic efficient prediction method based on a space-time aggregation graph convolutional network, which comprises the following steps of: firstly, dividing a prediction region into a plurality of sub-regions by the aggregation graph convolutional network, taking the sub-regions as nodes in the network, and performing modeling on a daily historical mode and a current mode per hour of mobile traffic, and capturing the complex space-time correlation of all nodes across different time. And then, the outputs of the K layers of aggregation graph convolutional network modules are connected through an embedded module. And then, fusing the prediction information with external features extracted by an external module by using a regression module to obtain a final mobile traffic prediction result, and updating model parameters to obtain a minimum loss function. According to the invention, the prediction performance of the mobile cellular traffic is effectively improved.

Description

technical field [0001] The invention relates to the technical field of mobile communication, in particular to a method for efficiently predicting mobile cellular traffic based on a spatio-temporal aggregation graph convolutional network. Background technique [0002] With the explosive growth of mobile devices and the rapid development of 5G communication networks, mobile cellular traffic forecasting has become a key component in network management. Accurate and timely traffic forecasting can help operators plan and optimize network resources and configuration, thereby effectively reducing network congestion, improving service quality, and promoting intelligent communication. Many traditional forecasting methods, such as historical average, autoregressive integrated moving average, support vector regression, etc., focus on capturing the temporal correlation of mobile cellular traffic. However, these conventional methods cannot easily mine the complex nonlinear spatio-tempor...

AI Technical Summary

Problems solved by technology

These factors may complicate the spatio-temporal dependence of city-wide mobile traffic

While some studies apply graph convolutional networks to predict mobile cellular traffic, most methods require significant resources and time to train predictive models

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