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.

Active Publication Date: 2021-01-29
SOUTHEAST UNIV +1
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Problems solved by technology

Existing methods for regional flow forecasting mostly use a single time series model, which does not consider the spatial correlation in regional flow forecasting, and does not cover the periodic variability of flow in the limited input flow series, resulting in poor prediction accuracy. lower

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  • Regional network flow prediction method based on deep learning
  • Regional network flow prediction method based on deep learning
  • Regional network flow prediction method based on deep learning

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Embodiment Construction

[0075] 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.

[0076] The invention provides a method for predicting regional traffic based on deep learning. Aiming at the temporal correlation and spatial correlation of regional traffic, the spatio-temporal features are simultaneously extracted through 3D convolutional neural network and ConvLSTM. The method of time series extraction enables the forecasting network to learn the periodic change characteristics of the traffic sequence under the limited input sequence length. Finally, the final regional traffic prediction result is obtained through the multi-layer perceptron.

[0077] Step 1: Obtain the regional network traffic sequence, and count the traffic value used by it at each moment.

[0078] (1) Divide the network coverage area into N×M grid areas of 1km×1km, record the coor...

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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.

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 ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04W24/06H04W28/16H04L12/24G06N3/04G06N3/08
CPCH04W24/06H04W28/16H04L41/145H04L41/147G06N3/049G06N3/08G06N3/048G06N3/045
Inventor 潘志文徐佳璐刘楠尤肖虎
Owner SOUTHEAST UNIV
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