Regional Correlation Aided Mobile Traffic Prediction with Spatiotemporal Deep Learning
This work addresses mobile traffic prediction for proactive network management, offering a domain-specific incremental improvement.
The paper tackles the problem of mobile traffic prediction by addressing the limitation of ignoring geographical correlations in existing deep learning methods, resulting in up to 28% performance improvement over state-of-the-art approaches.
Mobile traffic data in urban regions shows differentiated patterns during different hours of the day. The exploitation of these patterns enables highly accurate mobile traffic prediction for proactive network management. However, recent Deep Learning (DL) driven studies have only exploited spatiotemporal features and have ignored the geographical correlations, causing high complexity and erroneous mobile traffic predictions. This paper addresses these limitations by proposing an enhanced mobile traffic prediction scheme that combines the clustering strategy of daily mobile traffic peak time and novel multi Temporal Convolutional Network with a Long Short Term Memory (multi TCN-LSTM) model. The mobile network cells that exhibit peak traffic during the same hour of the day are clustered together. Our experiments on large-scale real-world mobile traffic data show up to 28% performance improvement compared to state-of-the-art studies, which confirms the efficacy and viability of the proposed approach.