STTF: A Spatiotemporal Transformer Framework for Multi-task Mobile Network Prediction

Jiahui Gong, Yu Liu, Tong Li, Jingtao Ding, Zhaocheng Wang, Depeng Jin

Abstract: Accurately predicting mobile traffic and accessed user amount is of great importance to network resource allocation, energy saving, etc. However, due to the complicated environmental contexts and complex interaction between mobile traffic and connected users, mobile network prediction is still challenging. Besides, the existing works could not be applied to large-scale networks because of the limited hardware resources and unacceptable time cost. In this work, we propose the spatiotemporal transformer framework for the multi-task mobile network prediction. Our proposed model contains three key parts. First, to capture the complex interaction between mobile traffic and connected users, we propose the temporal cross-attention encoder. Then, to identify and extract the most relevant information from various semantic relationships, we propose the hierarchical spatial encoder. This information is then used to create a more comprehensive representation of the network. Finally, the subgraph sampling method could significantly reduce the amount of computing power required and have comparable performance to the methods that input the whole network, enabling the model for real-world applications. Extensive experiments demonstrate that our proposed model significantly outperforms the state-of-the-art models by over 17% in both mobile traffic prediction and connected user prediction.

External IDs:doi:10.1109/tmc.2024.3521245