Abstract
While traffic modeling and prediction are at the heart of providing high-quality telecommunication services in cellular networks and have attracted much attention, they have been proved as extremely challenging tasks. Due to the diverse network demand of Internet-based apps, the cellular traffic from an individual user can have a wide dynamic range. Given the observation, we propose to leverage deep learning techniques to explore latent features in individual user’s cellular traffic. However, it is unclear what kind of features are explorable. To answer this question, we conducted a one-month data collection campaign and carried out a thorough analysis over the generated dataset. We find that user traffic demonstrates clear spatial-temporal patterns which are essential to our future study that leverages these characteristics for traffic forecasting.
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