Cross-Domain Transfer Learning for Spatiotemporal Cellular Traffic Forecasting and Intelligent Resource Scheduling in 6G Networks
Keywords:
cross-domain transfer learning; cellular traffic forecasting; resource scheduling; 6G networks; spatiotemporal modeling; system governance; energy-aware operationAbstract
The transition toward sixth-generation cellular systems introduces unprecedented demands for fine-grained resource orchestration because network functions, spectrum assignments, edge computation, and energy budgets must continuously adapt to volatile traffic patterns. This paper presents a system-level analysis of cross-domain transfer learning as a mechanism for spatiotemporal cellular traffic forecasting and intelligent resource scheduling in 6G networks. The discussion examines how heterogeneous data sources, including mobility traces, application logs, environmental observations, and infrastructure telemetry, can be integrated through transferable representations without assuming identical distributions across geographical regions or time periods. The analysis emphasizes structural relationships among data ingestion, representation learning, forecasting, scheduling, and policy enforcement. It further explores the tensions between centralized model coordination and distributed inference, between specialization for local conditions and generalization across domains, and between predictive accuracy and operational robustness. Governance, fairness, sustainability, and deployment challenges are treated as first-order design considerations rather than as secondary constraints. The paper argues that cross-domain transfer learning should be embedded within a broader architectural framework that accounts for lifecycle management, accountability, regulatory compliance, and energy consumption. Through this systems perspective, the paper contributes a conceptual foundation for designing forecasting and scheduling architectures that are not only accurate but also governable, resilient, and aligned with the long-term requirements of intelligent network infrastructures.
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