This paper 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.
@article{arxiv.2304.09811,
title = {Analytic Network Traffic Prediction Based on User Behavior Modeling},
author = {Liangzhi Wang and Jiliang Zhang and Zitian Zhang and Jie Zhang},
journal= {arXiv preprint arXiv:2304.09811},
year = {2023}
}
Comments
This paper has been submitted to IEEE Networking Letters for possible publications