Knowledge-Augmented Spatial–Temporal Traffic Forecasting for Autonomous Network Management in 6G Systems
Keywords:
autonomous network management; 6G systems; spatial-temporal traffic forecasting; knowledge augmentation; zero-touch networks; robustness; fairness; sustainabilityAbstract
The vision of sixth-generation (6G) networks encompasses extreme heterogeneity, near-instantaneous reconfigurability, and autonomous closed-loop management capable of orchestrating resources without human intervention. Central to this ambition is the ability to forecast network traffic with high fidelity across both spatial and temporal dimensions, enabling proactive and predictive resource allocation. While pure data-driven deep learning models have demonstrated remarkable accuracy in spatial-temporal traffic prediction, they often operate as black boxes, neglecting rich external knowledge such as infrastructure semantics, event calendars, social dynamics, and physical environment constraints. Knowledge-augmented forecasting, which fuses structured and unstructured external knowledge with neural spatial-temporal models, has emerged as a promising alternative to overcome these limitations. This paper presents a system-level examination of knowledge-augmented spatial-temporal traffic forecasting for autonomous network management in 6G systems. We articulate an end-to-end architectural vision that integrates heterogeneous knowledge sources, including large language models and knowledge graphs, within frameworks such as the O-RAN RAN Intelligent Controller and ETSI Zero-touch Network and Service Management. The discussion systematically addresses structural trade-offs between centralized and edge-distributed knowledge processing, the computability-latency-accuracy envelope under 6G latency budgets, and the infrastructural requirements for sustainable deployment. An in-depth governance analysis treats robustness against knowledge poisoning and adversarial perturbations, and fairness in resource allocation when predictions incorporate unevenly distributed external knowledge. We further reflect on the sustainability footprint of augmented prediction pipelines and propose strategies for life-cycle-aware design. The paper concludes by outlining future research directions that merge system-level trustworthiness with high-performance predictive autonomy.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
