Deep Transfer Learning for Spatiotemporal Cellular Traffic Forecasting and Intelligent 6G Network Resource Optimization
Keywords:
deep transfer learning, spatiotemporal forecasting, 6G networks, resource optimization, robustness, fairness, network governanceAbstract
The transition to sixth-generation wireless systems requires network management architectures capable of anticipating highly dynamic spatiotemporal demand while coordinating heterogeneous resources under stringent reliability, latency, and energy constraints. Deep transfer learning offers a pathway to improve cellular traffic forecasting across regions, service types, and operational periods by reusing cross-domain representations and reducing dependence on abundant target-domain data. This paper examines the integration of deep transfer learning with spatiotemporal forecasting and intelligent resource optimization in 6G networks from a systems perspective. The discussion emphasizes architectural decomposition, cross-domain adaptation, closed-loop resource control, and the structural trade-offs that emerge when predictive models are coupled with autonomous network operations. The paper further analyzes robustness, fairness, governance, deployment, sustainability, and policy implications. It argues that advanced forecasting must be embedded within control-theoretic safeguards, auditable decision pipelines, and polycentric governance arrangements to avoid brittle automation and inequitable resource allocation. Rather than focusing narrowly on algorithmic accuracy, the paper develops a socio-technical perspective in which transferable learning, resource optimization, and institutional oversight are jointly designed. This approach supports resilient 6G infrastructures that can adapt to distributional shift, respect operational constraints, and remain accountable to diverse stakeholders.
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