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5G networks get goal-oriented forecasting for PRB allocation

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new goal-oriented probabilistic forecasting framework to optimize physical resource block (PRB) allocation in 5G networks. This approach utilizes DeepAR and Temporal Fusion Transformer models trained with the Pinball Loss function to minimize operational costs by accounting for the asymmetric costs of under-provisioning versus over-provisioning. Evaluations on a real 5G traffic dataset demonstrated that this method reduces operational expenses while ensuring calibrated uncertainty estimates, leading to a better balance between service reliability and resource efficiency. AI

IMPACT Improves efficiency and reliability in 5G network resource allocation through advanced forecasting.

RANK_REASON Research paper published on arXiv detailing a new forecasting framework for 5G networks. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

5G networks get goal-oriented forecasting for PRB allocation

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Research paper published on arXiv detailing a new forecasting framework for 5G networks. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Oier Larumbe-Lizarraga, Roberto Pereira, Cristian J. Vaca-Rubio ·

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

    arXiv:2609.17297v1 Announce Type: cross 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-provisi…

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