Graph Neural Network and Spatial-Temporal Foundation Model Integration for Intelligent Mobile Network Traffic Prediction
Keywords:
Graph neural networks; spatial-temporal foundation models; mobile network traffic prediction; model integration; AI governance; sustainable AIAbstract
The rapid growth of mobile data traffic and the evolution toward ultra-dense and heterogeneous network architectures demand a fundamental shift in how network intelligence is realized. This paper presents a system-level investigation of the integration of graph neural networks (GNNs) with spatial-temporal foundation models for intelligent mobile network traffic prediction. Moving beyond algorithmic novelty, the analysis focuses on structural trade-offs, architectural design patterns, data infrastructure requirements, and governance mechanisms that shape the feasibility and trustworthiness of such integrated systems. We examine how GNNs can encode dynamic topological relationships among base stations and user mobility patterns, while large-scale spatial-temporal foundation models capture complex temporal dependencies across multiple scales and network regions. The fusion of these two paradigms opens new pathways for highly accurate and generalizable traffic forecasting, yet it introduces profound challenges related to computational sustainability, model interpretability, fairness across diverse geographic and demographic segments, and alignment with evolving regulatory frameworks. The paper discusses deployment strategies spanning centralized cloud, multi-access edge computing, and on-device inference, highlighting the tension between latency constraints and energy consumption. It further elaborates on data governance imperatives in the context of privacy-preserving, cross-operator learning and on the necessity of rigorous model auditing to ensure robustness against distributional shifts and adversarial perturbations. By synthesizing insights from network engineering, artificial intelligence, and technology policy, this work advances a holistic perspective that can guide the responsible integration of foundation model capabilities into critical communication infrastructures.
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