A dynamic heterogeneous network traffic prediction method based on a deep space-time neural network

Abstract

The invention belongs to the technical field of wireless communication, and particularly relates to a dynamic heterogeneous network flow prediction method based on a deep space-time neural network. Aiming at the problems of small coverage area, low prediction precision, short prediction time and the like of the existing mobile data traffic prediction method, the dynamic heterogeneous network traffic prediction method based on the deep space-time neural network is studied. Considering the characteristics of user mobility, flow data space-time correlation and the like, deeply researching a wide-coverage long-term mobile data flow prediction mathematical model description method in the dynamic heterogeneous network; On the basis, a space-time related convolutional long-short time memory network model is studied to predict the long-term trend of the mobile traffic in the dynamic heterogeneous network; A space-time related three-dimensional convolutional neural network model is studied to capture micro-fluctuation of a mobile flow sequence in the dynamic heterogeneous network; And fusing the long-term trend prediction model and the short-term change model of the mobile traffic, therebyrealizing wide-coverage and high-precision long-term mobile traffic prediction in the dynamic heterogeneous network.

Description

technical field [0001] The invention belongs to the technical field of wireless communication, and in particular relates to a dynamic heterogeneous network flow prediction method based on a deep spatio-temporal neural network. Background technique [0002] In order to meet the increasing transmission rate and mobile data volume requirements of mobile users, heterogeneous networks deploy different types of low-power small cells on the basis of traditional macro cells, and the system capacity and network coverage capabilities are significantly improved. In order to meet the demand for on-demand traffic offloading in hotspot areas, small cells need to optimize communication parameters in advance to meet instant communication requests, and UAV base stations must be deployed in hotspot areas in advance by adjusting their locations. Therefore, with the help of powerful data analysis methods of machine learning, traffic congestion events can be predicted and the gap between supply ...

AI Technical Summary

Problems solved by technology

[0005] In order to overcome the problems of small coverage area, low prediction accuracy, and short prediction time of existing mobile data traffic forecasting methods, the purpose of the present invention is to propose a dynamic heterogeneous network traffic forecasting method based on deep spatio-temporal neural network

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