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Long-term network traffic forecasting method based on deep learning

A network traffic, deep learning technology, applied in the field of wireless communication, can solve the problems of long-term prediction accuracy decline, error accumulation, poor effect, etc., to achieve the effect of being conducive to reasonable distribution, improving accuracy, and reducing data inconsistency

Active Publication Date: 2022-04-15
SOUTHEAST UNIV +1
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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.

Method used

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  • Long-term network traffic forecasting method based on deep learning
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Embodiment Construction

[0062] In order to describe the technical solution disclosed in the present invention in detail, further elaboration will be made below in conjunction with the accompanying drawings and specific embodiments.

[0063] The invention provides a long-term prediction method of network traffic based on deep learning. Aiming at the temporal correlation and spatial correlation of dynamic changes in network traffic, and the long-term memory ability of historical flow sequences, which is especially important in long-term network traffic forecasting, the Transformer model is used to extract spatio-temporal features and model global dependencies. And in order to alleviate the problem of inconsistent distribution of training data and test data, an adaptive training mechanism is proposed, and the input data is selected adaptively according to the error value in the training process, so as to maintain the balance of training data and test data. Improve the accuracy of long-term traffic forec...

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Abstract

The invention discloses a long-term network traffic prediction method based on deep learning, which includes: first obtaining the regional network traffic sequence, and counting the traffic value used in each moment; then preprocessing the traffic matrix sequence to obtain the input data of the Transformer model ; Secondly, establish a Transformer model, and use the Transformer model to adaptively extract temporal and spatial correlations for two-dimensional matrix data; finally, use an adaptive training mechanism for model training. The invention improves the accuracy of multi-step long-term forecasting of network flow, facilitates operators to plan future network resources in advance, and is beneficial to rational allocation of wireless resources.

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...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04W24/00H04W72/04H04L41/142H04L41/147G06N3/04G06N3/08
CPCH04W24/00H04L41/142H04L41/147G06N3/04G06N3/08H04W72/52
Inventor 潘志文徐佳璐刘楠尤肖虎
Owner SOUTHEAST UNIV
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