Wireless Network Traffic Prediction Method Based on Multi-Graph Convolution

Abstract

The present invention proposes a wireless network traffic prediction method based on multi-graph convolution. First, spatially construct adjacent graphs, functional similarity graphs, and spatial traffic correlation graphs, and use graph convolutional networks to extract spatial features; secondly, Construct recent, daily and weekly related flows in the time domain, input the three kinds of time domain flows into the long-term and short-term memory network, and finally add the attention mechanism to judge the importance of the flow at different times. The root mean square, mean absolute error and coefficient of determination are introduced to evaluate the predictive performance of the proposed model. The method of the invention has the characteristics of reasonable modeling, high prediction accuracy and the like.

Description

Wireless network traffic prediction method based on multi-graph convolution technical field The invention belongs to the technical field of wireless communication, graph theory and deep learning, particularly graph convolutional network, long-term and short-term memory memory network, attention mechanism, etc., especially a multi-graph convolution-based wireless network traffic prediction method. Background technique [0002] With the advent of the 5G era, great changes have taken place in wireless networks, especially a large number of wireless With the dramatic rise in line cellular data, accurate wireless network traffic forecasting is critical for operators; With dense base station distribution, it is an unprecedented challenge to achieve more efficient traffic prediction. To achieve traffic forecasting, the first It is necessary to know the spatiotemporal characteristics of traffic and the distribution of related data, and make reasonable and accurate predictions...

AI Technical Summary

Problems solved by technology

Traditional traffic forecasting methods are not good in accuracy and efficiency. With the rapid development of artificial intelligence, various fields are gradually exploring the introduction of artificial intelligence technology into research in this field.

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