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CellPred: A Behavior-aware Scheme for Cellular Data Usage Prediction
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DOI：10.1145/3380982.png)
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Cellular data usage consumption prediction is an important topic in cellular networks related researches. Accurately predicting future data usage can benefit both the cellular operators and the users, which can further enable a wide range of applications. Different from previous work focusing on statistical approaches, in this paper, we propose a scheme called CellPred to predict cellular data usage from an individual user perspective considering user behavior patterns. Specifically, we utilize explicit user behavioral tags collected from subscription data to function as an external aid to enhance the user's mobility and usage prediction. Then we aggregate individual user data usage to cell tower level to obtain the final prediction results. To our knowledge, this is the first work studying cellular data usage prediction from an individual user behavior-aware perspective based on large-scale cellular signaling and behavior tags from the subscription data. The results show that our method improves the data usage prediction accuracy compared to the state-of-the-art methods; we also comprehensively demonstrate the impacts of contextual factors on CellPred performance. Our work can shed light on broad cellular networks researches related to human mobility and data usage. Finally, we discuss issues such as limitations, applications of our approach, and insights from our work.
Keyword:
Urban Computing
Cellular networks
Deep Learning
Behaviors
Prediction
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