Predicting Mobile Users Traffic and Access-Time Behavior Using Recurrent Neural Networks
Predicting Mobile Users Traffic and Access-Time Behavior Using Recurrent Neural Networks
复制标题
使用循环神经网络预测移动用户流量和访问时间行为
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Deshi Li
中科院分区：
文献类型：
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作者：
A. Alamoudi;Mingliu Liu;Ali Payani;F. Fekri;Deshi Li
预测移动的用户的Web访问行为可以对无线网络的资源分配和成本降低产生实质性影响。因此，我们提出了一个机器学习平台来预测移动的用户的Web流量和访问时间。根据观察，流量模式表现出复杂的依赖于时间，位置和流行的网页。因此，基于不同的工程特征开发了具有长短期记忆（LSTM）的循环神经网络（RNN），以学习和预测用户的网络浏览活动。然后，为了预测移动的用户的未来访问时间，我们提出了一种自激励记忆神经网络（SMNN）。访问活动被建模为自激点过程，并采用强度进行预测。此外，我们将所提出的预测框架扩展到蜂窝塔。为了应对在蜂窝塔的流量的多样性，我们采取聚类方法来组每个塔的相似用户。然后，我们分别为每个集群开发一个LSTM模型，以预测蜂窝塔的Web域流量活动。最后，我们证明了我们提出的模型优于基于细胞网络数据集的基线预测模型。我们还表明，对于蜂窝塔接入预测任务，聚类方法可以显着提高预测精度。
Predicting mobile users’ web-access behavior can have substantial impacts on resource allocation and cost reduction for wireless networks. Therefore, we propose a machine learning platform to forecast the web traffic and access time of mobile users. Based on the observation, the traffic patterns exhibit complex dependency on time, location, and popularity of webpages. Thus, a Recurrent Neural Network (RNN) with Long Short Term Memory (LSTM) is developed based on distinct engineered features to learn and predict users’ web browsing activities. Then, to forecast the future access-time of mobile users, we propose a Self-exciting Memory Neural Network (SMNN). The access activities are modeled as self-exciting point processes, and intensities are adopted for prediction. Moreover, we extend the proposed predicting framework to cell towers. To cope with the diversity of traffic at the cell tower, we resort to clustering methods to group the similar users of each tower. Then, we develop an LSTM model for each cluster separately to predict the web domain traffic activities for the cell tower. Finally, we show that our proposed models outperform the baseline prediction models based on cellular networks dataset. We also show that for the cell tower access prediction task, the clustering method can significantly improve the prediction accuracy.