Cross-Domain Traffic Intelligence: Integrating Large Language Models and Graph Neural Networks for Wireless Network Forecasting and Optimization
Keywords:
Large language models, graph neural networks, wireless network optimization, traffic forecasting, cross-domain intelligence, socio-technical systems, network sustainabilityAbstract
The escalating complexity of next-generation wireless networks demands intelligent forecasting and optimization mechanisms capable of assimilating heterogeneous data streams across multiple spatial and temporal scales. This paper investigates a cross-domain traffic intelligence paradigm that integrates large language models and graph neural networks to unlock unprecedented capabilities in wireless network management. Large language models offer powerful semantic reasoning over unstructured contextual information including social media, event schedules, and weather reports, while graph neural networks excel at capturing structural dependencies among network nodes and spatiotemporal traffic patterns. We present a system-level architectural framework that orchestrates these two technologies through a hybrid pipeline, addressing critical design trade-offs in data fusion, computational partitioning, and model synchronization. The study delves deeply into governance, infrastructure, and deployment dimensions that are often overlooked in purely algorithmic treatments, covering privacy-preserving data sharing, federated learning across administrative domains, and regulatory compliance. Sustainability is examined through the lens of energy consumption and lifecycle management of large-scale models, while fairness and robustness are analyzed as fundamental socio-technical requirements for operational deployments. A cross-domain comparative analysis contrasts the proposed integration against conventional statistical and deep learning baselines across cellular, vehicular, and IoT ecosystems. The paper concludes by outlining a forward-looking research agenda that emphasizes foundation models for wireless networks, interpretable intelligence, and responsible automation governed by human-centric policies. The findings provide a blueprint for building resilient, equitable, and efficient traffic intelligence systems in the 6G era and beyond.
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