A prediction model of massive 5G network users’ revisit behavior based on telecom big data

Yudi SUN

doi:10.11959/j.issn.1000-0801.2023026

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A prediction model of massive 5G network users’ revisit behavior based on telecom big data

Research and Development | 更新时间：2024-06-05

- A prediction model of massive 5G network users’ revisit behavior based on telecom big data
- Telecommunications Science Vol. 39, Issue 2, Pages: 157-162(2023)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2023026
  CLC： TP357
- Published Online：2023-02，
  
  Published：20 February 2023
- 稿件说明：
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Yudi SUN. A prediction model of massive 5G network users’ revisit behavior based on telecom big data[J]. Telecommunications science, 2023, 39(2): 157-162.
DOI：

Yudi SUN. A prediction model of massive 5G network users’ revisit behavior based on telecom big data[J]. Telecommunications science, 2023, 39(2): 157-162. DOI： 10.11959/j.issn.1000-0801.2023026.

摘要

5G网络中的用户会产生大量的访问数据，导致用户复访行为难以精准预测，因此提出基于电信大数据的5G网络海量用户复访行为预测模型。从电信大数据中提取用户上网历史行为特征数据，构建数据集。引入多阶加权马尔可夫链模型，通过计算各阶自相关系数，得到模型权重值，计算模型的统计量。经过分析后得到各阶步长的马尔可夫氏链一步转移概率矩阵，从而实现对5G网络海量用户复访行为的精准预测。实验结果表明，该模型拥有最低的均值误差和标准差，以及最高的精度、查全率、查准率、F1指标，可证明该方法在预测用户复访行为方面有着非常明显的优势。

Abstract

Users in 5G networks will generate a large amount of access data

which makes it difficult to accurately predict users’ revisit behavior.Therefore

a prediction model of massive 5G network users’ revisit behavior based on telecom big data was proposed.The user’s historical online behavior characteristic data was extracted from the telecom big data to build a data set.Multi order weighted Markov chain model was introduced.The model weight value was obtained by calculating the autocorrelation coefficient of each order

and the statistics of the model were calculated.After analysis

the one-step transition probability matrix of Markov chain with each step size was obtained

so as to accurately predict the revisit behavior of massive users in 5G network.The experimental results show that the proposed model has the lowest mean error and standard deviation

as well as the highest accuracy

recall

precision and F1 indicators

which can prove that the proposed method has a very obvious advantage in predicting users’ revisit behavior.

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Keywords

references

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