5G网络中面向目标的概率预测用于动态PRB分配
Goal-oriented probabilistic forecasting for dynamic PRB allocation in 5G networks
这个研究很实用，解决了5G网络资源分配的问题，用概率预测模型来平衡服务可靠性和资源效率，比传统方法更精准。
研究提出了一种面向目标的概率预测框架，用于5G网络中物理资源块（PRB）的动态分配。该框架使用Pinball Loss函数训练DeepAR和Temporal Fusion Transformer（TFT）模型，并从运营商的成本矩阵中推导出最优分配分位数。在真实5G流量数据集上的评估显示，与基于均方误差（MSE）训练的基线相比，该方法降低了运营成本，同时保持了校准的不确定性估计。
Goal-oriented probabilistic forecasting for dynamic PRB allocation in 5G networks
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.