Service prediction method and device for energy-saving cell

Abstract

The embodiment of the invention provides a service prediction method and device for an energy-saving cell, relates to the field of communication, and is used for predicting a service load in a commoncoverage cell set and improving the accuracy of predicting the service load of the energy-saving cell. The method comprises the following steps: acquiring a first service load of a compensation cell in a target time period, wherein the first service load is the total service load of the energy-saving cell and the compensation cell in the target time period; and determining a second service load ofthe energy-saving cell in the target time period according to the network types of the energy-saving cell and the compensation cell and the first service load, wherein the second service load is usedfor indicating the actual service load of the energy-saving cell in the target time period. The method and the device are used for predicting the service load of the cell.

Description

Technical field [0001] The present invention relates to the field of communications, and more particularly to a service prediction method and apparatus for a energy saving cell. Background technique [0002] In the current energy-saving technique, the business load of its future time period is typically predicted according to the historical business load of the cell to determine the idle period of the cell, thereby performing corresponding software energy saving for the community in the idle period. [0003] However, in the network-level energy-saving system, the influence of service transfer is predicted between the energy-saving cell and the compensation cell, and the predicted business load cannot really reflect the energy-saving community and compensation communities according to the business load of the energy-saving cell and the compensating cell. Business load. For example, when acquiring the current cell cell1 N days of historical traffic load, if the saving operation is ...

AI Technical Summary

Problems solved by technology

[0003] However, in the network-level energy-saving system, due to the impact of business transfer between energy-saving cells and compensation cells, when predicting the business loads of energy-saving cells and compensation cells based on historical traffic loads, the predicted traffic load cannot truly reflect the energy-saving cells and compensation cells. business load

For example, when obtaining the historical traffic load of cell1 for the current N days, if cell1 does not perform energy-saving operations within the N days, the historical traffic load can truly reflect the traffic load change trend of cell1 in the N days, and at this time, the The historical business load predicts the business load of cell1 in the future time period; if cell1 performs energy-saving operations within the N days, the business load of cell1 may be migrated to the compensation cell, so that the historical business load within the N days cannot truly reflect the business load of cell1 Change trend. At this time, the business load predicted for cell1 in the future using the historical business load cannot truly reflect the business load of cell1.

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