A method, system, device and medium for forecasting wireless traffic during holidays and holidays based on a correlation clustering hybrid algorithm model

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 traffic usage experience

Active Publication Date: 2022-06-07
SHANDONG UNIV
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  • Abstract
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  • 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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  • A method, system, device and medium for forecasting wireless traffic during holidays and holidays based on a correlation clustering hybrid algorithm model
  • A method, system, device and medium for forecasting wireless traffic during holidays and holidays based on a correlation clustering hybrid algorithm model
  • A method, system, device and medium for forecasting wireless traffic during holidays and holidays based on a correlation clustering hybrid algorithm model

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Experimental program
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Effect test

Embodiment 1

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

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

[0072] Step 2) Calculate 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 holiday coefficients;

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

Embodiment 2

[0078] A holiday wireless traffic prediction method based on a correlation clustering hybrid algorithm model provided according to Embodiment 1, the difference is:

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

[0080] (1) When forecasting traffic on holidays other than the Spring Festival: the weight of holidays for working days is 0, the weight of non-holiday weekends and winter and summer vacations is 1, the weight of holidays for days with a holiday duration less than or equal to three days is 3, and the duration of holidays is greater than three The holiday weight of the date of the day is 5, 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 one of the most important festivals in China, its duration is also different from the general holidays. Although the period be...

Embodiment 3

[0121] A holiday wireless traffic prediction method based on a correlation clustering hybrid algorithm model provided according to Embodiment 2, the difference is:

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

[0123] When the prediction result shows that the traffic usage of a base station sector increases by more than 50% during holidays, expand the resources of the base station sector or increase the power of the base station sector 3 days in advance to ensure the user experience;

[0124] When the forecast result shows that the traffic usage of a base station sector is reduced by more than 50% during holidays, reduce the power of the base station sector, reduce operating costs, and operate with low carbon.

[0125] Use the traffic data of the actual operator to perform performance verification on the model proposed by the present invention. The performance verification is to compare the d...

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Abstract

The invention relates to a method, system, device and medium for forecasting wireless traffic on holidays based on a correlation clustering hybrid algorithm model. The forecasting method for wireless traffic on holidays includes: step 1) defining holiday weights according to different dates; step 2) according to holidays Weight calculation of the holiday coefficient of the base station sector; step 3) classifying the traffic patterns of all base station sectors according to the holiday coefficient; step 4) constructing a hybrid algorithm model; step 5) obtaining the well-trained hybrid algorithm model corresponding to different traffic patterns; Step 6) Use the trained hybrid algorithm model to predict the wireless traffic of the base station sector. The present invention proposes to divide the base station sectors into different traffic patterns by holiday coefficients, and train a common model framework for each pattern, which can effectively solve the problem of over-fitting, and make the model have flexibility and the ability to capture the characteristics of traffic changes.

Description

technical field [0001] The invention relates to a holiday wireless traffic prediction method, system, equipment and medium based on a correlation clustering mixed algorithm model, belonging to the technical field of wireless traffic prediction. Background technique [0002] With the popularity of devices such as smartphones, wireless cellular networks are playing an increasingly important role. Accurately predicting the flow of base station sectors has important reference significance for base station resource deployment and power allocation, and is an important prerequisite for realizing intelligent communication networks. 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 out-of-home workers will return to their hometowns during the holidays, and they are the main force using traffic. This 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 Patents(China)
IPC IPC(8): H04W24/06H04L41/147H04L41/14H04L41/142G06K9/62G06N3/04
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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