Method and device for predicting network traffic

Abstract

The invention provides a method and a device for predicting network traffic. The method comprises: acquiring cell-level traffic historical data of the ith cell in the network in the past M months, and calculating month-to-month growth rates of the cell-level traffic of the ith cell in the past M months according to the obtained cell-level traffic historical data, wherein the M is a positive integer greater than or equal to 3, and the i is a positive integer; predicting a month-to-month growth rate of the cell-level traffic of the ith cell in a scheduled month L in the future according to the month-to-month growth rates of the cell-level traffic of the ith cell in the past M months; and acquiring the cell-level traffic of the ith cell in the scheduled month L according to the month-to-month growth rate of the cell-level traffic of the ith cell in the scheduled month L in the future and the cell-level traffic historical data of the ith cell in the past M months. The device of the invention comprises a calculation module, a prediction module, and an acquisition module.

Description

technical field [0001] The embodiments of the present invention relate to the technical field of communications, and in particular, to a method and device for predicting network traffic. Background technique [0002] With the rapid development of mobile communications, the number of mobile users has grown explosively, and the traffic volume of mobile communication networks has also continued to rise, making the wireless network load saturated or even congested. If the operator can accurately and effectively predict the network traffic volume, and rationally allocate network resources according to the predicted network traffic volume, the purpose of reducing network congestion rate and improving user satisfaction can be achieved. [0003] figure 1 It is a flowchart of a method for predicting network traffic in the prior art, such as figure 1 As shown, the prior art can realize network traffic prediction through the following five steps: [0004] Step 1. Collect historical ...

AI Technical Summary

Problems solved by technology

[0010] The network traffic forecasting method provided by the existing technology cannot effectively reflect the difference in traffic growth characteristics at each cell level. The future traffic growth rate of all cells is equal to the growth rate of the entire network traffic, ignoring the mobile communication network. The essential feature is that the business volume is not balanced geographically

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