Festival and holiday wireless flow prediction method, system and device based on correlation clustering hybrid algorithm model and medium

A correlation clustering and hybrid algorithm technology, applied in the field of wireless traffic forecasting, can solve problems such as error interference and limited traffic forecasting effect, reduce the number of models, and improve the effect of traffic usage experience

Active Publication Date: 2021-06-11
SHANDONG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, when predicting the traffic value during the Spring Festival, because the traffic pattern during the Spring Festival varies greatly from the usual time, the traffic during the non-holiday period has limited effect on the traffic forecast during the Spring Festival, and sometimes even causes false interference

Method used

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  • Festival and holiday wireless flow prediction method, system and device based on correlation clustering hybrid algorithm model and medium
  • Festival and holiday wireless flow prediction method, system and device based on correlation clustering hybrid algorithm model and medium
  • Festival and holiday wireless flow prediction method, system and device based on correlation clustering hybrid algorithm model and medium

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0070] A holiday wireless traffic prediction method based on a correlation clustering hybrid algorithm model, which is used to predict the traffic of a base station sector, such as Figure 8 As shown, the specific steps include:

[0071] Step 1) Define the holiday weights according to the different dates; the importance of each day is represented by the holiday weights, and the higher the holiday coefficient, the indicating that the date is an important holiday;

[0072] Step 2) calculating the holiday coefficient of the base station sector according to the holiday weight;

[0073] Step 3) Classify the traffic patterns of all base station sectors according to the holiday coefficient;

[0074] Step 4) Use linear regression algorithm, long short-term memory algorithm, random forest regression algorithm, and support vector regression algorithm to jointly construct a hybrid algorithm model; choose multiple algorithms to jointly construct a hybrid algorithm model in order to simul...

Embodiment 2

[0078] According to the holiday wireless traffic forecasting method based on the correlation clustering hybrid algorithm model provided in Embodiment 1, the difference is that:

[0079] In step 1), the holiday weights are defined according to different dates, specifically:

[0080] (1) When forecasting traffic on holidays other than the Spring Festival: the weight of holidays on weekdays is 0, the weight of non-holiday weekends and winter and summer vacations is 1, the holiday weight of days with holidays less than or equal to three days is 3, and the holidays with a length of more than three days The holiday weight of the day's date is 5, and the holiday weight of the seven days before the Spring Festival is assigned as 3, and the holiday weight of the seven days after the Spring Festival is assigned as 7; since the Spring Festival is the most important festival in China, its duration is also different from general holidays. Although the period before the Spring Festival is n...

Embodiment 3

[0121] According to the holiday wireless traffic forecasting method based on the correlation clustering hybrid algorithm model provided in Embodiment 2, the difference is that:

[0122] According to the wireless prediction result obtained in step 6), the base station is adjusted and deployed, specifically:

[0123] When the prediction results show that the traffic usage of a certain base station sector increases by more than 50% during holidays, the resource expansion of the base station sector or the power of the base station sector shall be carried out 3 days in advance to ensure the user experience;

[0124] When the prediction results show that the traffic usage of a certain base station sector is reduced by more than 50% during holidays, the power of the base station sector is reduced to reduce operating costs and achieve low-carbon operation.

[0125] Use the traffic data of actual operators to perform performance verification on the model proposed by the present inventi...

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Abstract

The invention relates to a holiday and festival wireless flow prediction method, system and device based on a correlation clustering hybrid algorithm model, and a medium. The holiday and festival wireless flow prediction method comprises the following steps: 1) respectively defining holiday and festival weights according to different dates; step 2) calculating holiday and festival coefficients of a base station sector according to holiday and festival weights; 3) classifying the flow modes of all base station sectors according to holiday and festival coefficients; 4) constructing a hybrid algorithm model; 5) obtaining trained hybrid algorithm models corresponding to different flow modes; and step 6) using the trained hybrid algorithm model to carry out wireless traffic prediction on the base station sector. According to the method, a base station sector is divided into different flow modes through holiday and festival coefficients, and a common model architecture is trained for each mode, so that the problem of overfitting can be effectively solved, and the model has flexibility and the capacity of capturing flow change characteristics.

Description

technical field [0001] The invention relates to a holiday wireless traffic prediction method, system, equipment and medium based on a correlation clustering hybrid algorithm model, and belongs to the technical field of wireless traffic prediction. Background technique [0002] With the popularity of devices such as smartphones, wireless cellular networks play an increasingly important role. Accurately predicting the traffic of a base station sector has important reference significance for base station resource deployment and power allocation, and is an important prerequisite for realizing an intelligent communication network. Especially during the holidays, due to the large flow of population, the traffic usage in many areas will change. For example, in many rural areas, many migrant workers will return to their hometowns during the holidays, and they are the main force of traffic usage. It will lead to a surge in wireless traffic usage in these areas during the holidays. ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04W24/06H04L12/24G06K9/62G06N3/04
CPCH04W24/06H04L41/147H04L41/145H04L41/142G06N3/044G06F18/2411G06F18/24323G06F18/241G06F18/254Y02D30/70
Inventor 张海霞康天宇袁东风梁聪梁道君
Owner SHANDONG UNIV
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