Network flow prediction method and device and electronic equipment

Abstract

The invention discloses a network flow prediction method and device, and electronic equipment. The network flow prediction method comprises the following steps: modeling a downlink of a cellular network based on a Poisson point process PPP to obtain a network model; establishing a corresponding relationship between a part of orthogonal channels in the plurality of orthogonal channels and the D2D users, and a corresponding relationship between the remaining orthogonal channels and the cellular users; and acquiring signal transmission parameter values at the cellular user and the D2D user, and if the signal transmission parameter values meet a preset condition, calculating the total network flow corresponding to the target time period in the network model according to the flow at the cellular user and the D2D user to obtain a network flow prediction result. According to the embodiment of the invention, the heterogeneous cellular network flow is analyzed and predicted based on the theoretical framework of random geometry, the prediction precision of the network flow is improved, and the maximization of the network flow is realized through spectrum resource allocation.

Description

technical field [0001] The present application relates to the technical field of communications, and in particular to a network traffic prediction method, device and electronic equipment. Background technique [0002] In recent years, with the rapid growth of mobile users and the explosive growth of data traffic, the existing network architecture has been greatly impacted. In order to achieve better network monitoring and network management, traffic statistics and forecasting have become one of the important technical methods of network security management. Traffic statistics forecasting is to establish a related network traffic model to predict the future by modeling historical traffic. of network traffic. [0003] In the existing literature, researchers have made in-depth analysis of network traffic from the aspects of fractal, cycle, chaotic characteristics summary, fractal application, multifractal, time series analysis, wavelet analysis, neural network, chaos theory, e...

AI Technical Summary

Problems solved by technology

The neural network as a non-stationary model can make up for the fact that the static model cannot characterize the non-stationary flow defect, but the neural network has no theoretical basis for the precise layer structure and the number of neurons

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