Method and device for traffic forecasting of mobile communication network

Abstract

The embodiment of the invention provides a traffic prediction method and device for a mobile communication network, relates to the field of communication, and aims to effectively increase the accuracy of a traffic estimated value. The traffic prediction method comprises the following steps: acquiring a paging amount of a first mobile communication network according to a paging message of the first mobile communication network, and acquiring traffic of the first mobile communication network according to service information of the first mobile communication network; acquiring a linear relationship model between the paging amount and the traffic by a linear regression principle according to the paging amount of the first mobile communication network and the traffic of the first mobile communication network; acquiring a paging amount of a second mobile communication network according to a paging message of the second mobile communication network, wherein the first mobile communication network is different from the second mobile communication network; and estimating an estimated value of the traffic of the second mobile communication network according to the paging amount of the second mobile communication network and the linear relationship model between the paging amount and the traffic. Through adoption of the traffic prediction method and device, the accuracy of the estimated value of the traffic is increased.

Description

technical field [0001] The invention relates to the communication field, in particular to a method and device for predicting traffic volume of a mobile communication network. Background technique [0002] At present, before building a mobile communication network, operators need to plan the network according to the network requirements of the target area, the network and business development of other operators, so as to reasonably build the mobile communication network. In the prior art, the subsequent traffic distribution is mostly predicted based on existing traffic data. For example, operators can refer to the traffic volume data included in the annual reports released by other operators for network planning. However, the traffic volume data in the annual reports are too macroscopic and have a certain lag, and cannot reflect a certain location area or the entire local area. Network traffic distribution. Therefore, the estimation of traffic volume is less accurate. Con...

AI Technical Summary

Problems solved by technology

For example, operators can refer to the traffic volume data included in the annual reports released by other operators for network planning. However, the traffic volume data in the annual reports are too macroscopic and have a certain lag, and cannot reflect a certain location area or the entire local area. Network traffic distribution

As a result, estimates of traffic volume are less accurate

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