Transfer Learning-Enabled Intelligent Network Traffic Prediction for Adaptive Resource Allocation in Heterogeneous 6G Wireless Networks
Keywords:
6G wireless networks; transfer learning; network traffic prediction; adaptive resource allocation; edge intelligence; network slicing; robustnessAbstract
The emergence of sixth-generation wireless systems is marked by profound heterogeneity in spectrum bands, access technologies, deployment densities, and service requirements. In such environments, conventional traffic prediction and resource allocation methods that depend on stationary assumptions and abundant local training data become increasingly unreliable. Transfer learning provides a mechanism to reuse representations learned from related domains, thereby reducing data requirements, accelerating adaptation, and improving prediction robustness under distributional shifts. This paper presents a system-level investigation of transfer learning-enabled intelligent network traffic prediction for adaptive resource allocation in heterogeneous 6G wireless networks. The study examines architectural foundations, cross-domain inference workflows, closed-loop orchestration structures, and deployment trade-offs. Rather than foregrounding algorithmic novelty, the analysis emphasizes structural tensions among centralized and edge-centric intelligence, model generality and domain specificity, prediction latency and accuracy, fairness and efficiency, and resilience and interpretability. The discussion further addresses robustness under concept drift, governance of data and models, sustainability implications of energy-aware resource management, and policy considerations for standardization. Drawing on cross-domain evidence from intelligent transportation, mobile edge computing, and network slicing, the paper argues that prediction-driven resource allocation should be treated as a distributed socio-technical infrastructure problem. The analysis closes by identifying research directions for accountable, fair, and sustainable learning-driven resource orchestration in 6G systems.
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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.
