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.
翻译:本文提出了一种基于用户行为的网络流量预测方法。根据用户行为,每周的流量需求可以被自然地划分为三类,即工作日、星期六和星期日。对于每一类,流量模式被分为三个组成部分,主要在三个时间段内生成,即早上、下午和晚上。每个组件都被建模为一个正态分布信号。数字结果表明,UBB NTP方法非常符合实际的无线流量需求。与现有方法相比,提出的UBB NTP方法提高了计算效率并增加了预测准确性。