Regional network flow prediction method based on deep learning

A technology for regional network and traffic prediction, which is applied in the field of regional network traffic prediction based on deep learning, and can solve the problems of not covering the periodic variability of traffic, low accuracy, and not considering the spatial correlation of regional traffic prediction.
CN112291808AActive Publication Date: 2021-01-29SOUTHEAST UNIV +1
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
SOUTHEAST UNIV
Publication Date
2021-01-29

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Abstract

The invention discloses a regional network flow prediction method based on deep learning, and the method comprises the steps: 1, obtaining a regional network flow sequence, and carrying out the statistics of a flow value of the regional network flow sequence at each moment; 2, extracting a flow matrix sequence with a corresponding characteristic as the input of a deep learning prediction model according to the spatial correlation and time correlation of the regional flow sequence, wherein the time correlation comprises compactness, periodicity and tendency; 3, for the three input flow matrix sequences obtained in the step 2, extracting time and space correlation by using a 3D convolutional neural network and ConvLSTM respectively; and 4, fusing the features, extracted from 3D convolution and ConvLSTM, of the three flow matrix sequences, and carrying out the final flow prediction based on an attention mechanism. According to the method, the periodic change characteristic of the flow sequence is covered under the limited input length through the time sequence extraction method, the regional network flow value at the next moment is predicted with high accuracy, reasonable distributionof wireless resources is facilitated, and the resource utilization rate is increased.
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Description

technical field

[0001] The invention belongs to the technical field of wireless communication, and in particular relates to a regional network traffic prediction method based on deep learning. Background technique

[0002] In recent years, with the rapid development of the fifth generation (5G) mobile communication technology, the application of innovative services such as AR (Augmented Reality, Augmented Reality) / VR (Virtual Reality, Virtual Reality), high-definition video, and automatic driving has made users The demand for web traffic has skyrocketed. In order to meet the stringent performance requirements of these services, accurate traffic engineering and network resource allocation become extremely important. Therefore, predicting and understanding mobile traffic based on big data is an important means to achieve intelligent allocation of wireless resources and improve the utilization of wireless resources. Existing methods for regional flow forecasting mostly use a ...

Claims

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