Goal-oriented probabilistic forecasting for dynamic PRB allocation in 5G networks

Abstract:Efficient physical resource block (PRB) allocation in 5G networks requires accurate demand forecasting. Conventional methods minimize symmetric error metrics (MAE, RMSE), ignoring the operational cost asymmetry where under-provisioning (service degradation) is far costlier than over-provisioning (wasted capacity). We propose a goal-oriented probabilistic forecasting framework that aligns model training with the operator's decision-making objectives. Specifically, we train DeepAR and Temporal Fusion Transformer (TFT) models using the Pinball Loss function and derive the optimal allocation quantile from the operator's cost matrix. Evaluation on a real beam-level 5G traffic dataset shows that the proposed approach reduces operational cost compared to MSE-trained baselines while maintaining calibrated uncertainty estimates. The framework enables dynamic PRB allocation that explicitly balances service reliability against resource efficiency.

Signal Processing (eess.SP); Machine Learning (cs.LG)

arXiv:2609.17297 [eess.SP]

(or arXiv:2609.17297v1 [eess.SP] for this version)

https://doi.org/10.48550/arXiv.2609.17297 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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