Probabilistic forecasting improves resource allocation in 5G networks
Goal-oriented probabilistic forecasting for dynamic PRB allocation in 5G networks
Machine Learning
Summary
Allocating resources in 5G networks can be tricky because running out of capacity hurts service more than having a bit too much. The authors study better ways to predict future demand by focusing on these real-world costs rather than just average errors. They use special forecasting methods trained to reduce costly under-provisioning. Tests on real data show their approach lowers operational costs while keeping reliable uncertainty estimates, helping balance service quality and efficient resource use.
What this means in practice
- •For network schedulers: Dynamically allocate physical resource blocks in 5G networks by using cost-aware forecasts to reduce service degradation and resource wastage.
- •For cloud infrastructure managers: Improve forecasting of computational demand under asymmetric cost constraints to optimize dynamic resource provisioning in cloud networks.
Authors
Oier Larumbe-Lizarraga, Roberto Pereira, Cristian J. Vaca-Rubio
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.