Cellular Traffic Prediction via Deep State Space Models with Attention Mechanism
This work addresses network resource management for cellular operators, representing an incremental improvement in domain-specific prediction.
The paper tackles cellular traffic prediction by proposing an end-to-end framework that combines convolutional neural networks with attention mechanisms and Kalman filters to capture spatiotemporal patterns, achieving improved prediction accuracy over state-of-the-art methods on three real-world datasets.
Cellular traffic prediction is of great importance for operators to manage network resources and make decisions. Traffic is highly dynamic and influenced by many exogenous factors, which would lead to the degradation of traffic prediction accuracy. This paper proposes an end-to-end framework with two variants to explicitly characterize the spatiotemporal patterns of cellular traffic among neighboring cells. It uses convolutional neural networks with an attention mechanism to capture the spatial dynamics and Kalman filter for temporal modelling. Besides, we can fully exploit the auxiliary information such as social activities to improve prediction performance. We conduct extensive experiments on three real-world datasets. The results show that our proposed models outperform the state-of-the-art machine learning techniques in terms of prediction accuracy.