Long-term network traffic prediction method based on deep learning

Abstract

The invention discloses a long-term network traffic prediction method based on deep learning, and the method comprises the steps: obtaining a regional network traffic sequence, and carrying out the statistics of a traffic value used at each moment; preprocessing the traffic matrix sequence, and obtaining the input data of a Transform model; establishing a Transform model, and carrying out adaptive extraction of temporal correlation and spatial correlation on the two-dimensional matrix data by adopting the Transform model; and finally, carrying out model training by adopting a self-adaptive training mechanism. According to the method, the accuracy of multi-step long-term prediction of the network flow is improved, so that an operator can plan future network resources in advance, and the reasonable allocation of wireless resources is facilitated.

Description

technical field [0001] The invention belongs to the technical field of wireless communication, and in particular relates to a long-term network traffic prediction method based on deep learning. Background technique [0002] In recent years, the fifth generation of mobile communication technology (5th Generation, 5G) has developed rapidly, representing the main direction of future network development, and will drive the society to gradually expand from the broadband interconnection between people to the Internet of everything, so as to become more It has profoundly affected the way of life and work of human society. The rapid development of mobile networks and the multiplication of traffic data continue to affect network performance and user experience, and pose new challenges for operators to rationally allocate base station resources and effectively ensure network stability and user experience. In order to meet the huge traffic demand, network operators and managers must s...

AI Technical Summary

Problems solved by technology

However, although the existing traffic forecasting models have achieved good results in short-term forecasting, they are not effective in long-term forecasting.

Due to the suddenness and randomness of the traffic sequence, it is difficult to capture its temporal and spatial correlation dynamically, and the existence of error accumulation makes the long-term prediction accuracy drop sharply with the increase of time.

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