Analytic Network Traffic Prediction Based on User Behavior Modeling

被引：0

作者：

Wang, Liangzhi ^{[3
]}

Zhang, Jiliang ^{[1
]}

Zhang, Zitian ^{[2
]}

Zhang, Jie ^{[3
,4
]}

机构：

[1] Northeastern University, College of Information Science and Engineering, Shenyang,110819, China

[2] Zhejiang Gongshang University, School of Information and Electronic Engineering, Hangzhou,314423, China

[3] The University of Sheffield, Department of Electronic and Electrical Engineering, Sheffield,S10 2TN, United Kingdom

[4] Ranplan Wireless Network Design Ltd, Cambridge,CB23 3UY, United Kingdom

来源：

IEEE Networking Letters | 2023年 / 5卷 / 04期

关键词：

Behavioral research - Computational efficiency - Data structures - Electronic mail - Forecasting - Traffic control;

D O I：

10.1109/LNET.2023.3278498

中图分类号：

学科分类号：

摘要：

This letter proposes an interpretable user-behavior-based (UBB) network traffic prediction (NTP) method. Based on user behavior, a weekly traffic demand profile can be naturally sorted into three categories, i.e., weekday, Saturday, and Sunday. For each category, the traffic pattern is divided into three components which are mainly generated in three time periods, i.e., morning, afternoon, and evening. Each component is modeled as a normal-distributed signal. Numerical results indicate the UBB NTP method matches the practical wireless traffic demand very well. Compared with existing methods, the proposed UBB NTP method improves the computational efficiency and increases the predictive accuracy. © 2019 IEEE.

引用

页码：208 / 212

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