Deep Learning-Driven Cellular Traffic Forecasting for Intelligent 6G Network Resource Allocation
Keywords:
cellular traffic forecasting; deep learning; 6G networks; resource allocation; network intelligence; fairness; governance; sustainabilityAbstract
The allocation of radio, transport, edge, and core resources in sixth-generation cellular systems is becoming increasingly dependent on predictive intelligence. This paper examines deep learning-driven cellular traffic forecasting as a system-level enabling technology for anticipatory resource orchestration. Rather than focusing on a single neural architecture, the analysis emphasizes the structural relationships among data infrastructure, spatiotemporal modeling, closed-loop control, deployment constraints, and institutional governance. It discusses how convolutional recurrent models, attention mechanisms, graph-based representations, and deep reinforcement learning can be organized into a coherent network intelligence fabric. The paper further explores trade-offs between forecast accuracy and operational costs, centralization and edge distribution, energy consumption and latency, and robustness and adaptability. It argues that forecasting models should not be treated as isolated analytical artifacts but as embedded components within a larger socio-technical system that includes telemetry pipelines, model lifecycle management, policy enforcement, and fairness mechanisms. Drawing on cross-domain lessons from autonomous vehicle control and distributed learning, the paper highlights the importance of bounded uncertainty, auditability, and lifecycle planning. The conclusion sets out research directions for sustainable and accountable 6G resource allocation systems.
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