PRB-RUPFormer：一种递归统一概率变压器用于残余PRB预测
PRB-RUPFormer: A Recursive Unified Probabilistic Transformer for Residual PRB Forecasting
AI总结：
本文提出PRB-RUPFormer模型，通过联合处理多变量KPI时间序列，利用时间、季节性和载波感知嵌入，实现残余PRB预测，降低计算开销并提供预测不确定性估计，实验表明其在LTE网络数据上具有较高的预测精度和可靠性。
摘要：
准确预测残余物理资源块（PRB）对于主动网络切片配置、节能运行和频谱感知决策至关重要，其中残余PRB作为短至中期内频谱可用性的实用代理。现有PRB预测方法通常仅依赖历史PRB值并针对每个载波或扇区独立训练，限制了其捕捉跨载波依赖性和提供预测不确定性的能力。此外，仅点预测在高度变化的交通条件下不足以支持鲁棒的频谱感知控制。本文提出PRB-RUPFormer，一种递归统一概率变压器用于残余PRB预测。所提出的模型通过时间、季节性和载波感知嵌入联合处理多变量KPI时间序列，在递归展开过程中保持指标间的时间耦合，并稳定长周期预测。在eNB的所有载波和扇区上训练一个共享模型，实现高效学习联合交通动态，具有低计算开销。通过分位数预测区间捕捉预测不确定性，提供对未来PRB可用性的置信度感知估计。在六个月内多个美国商业LTE网络数据上的评估显示，对于一天和七天递归预测，中位MAE低于0.05且命中概率高于0.80。这些概率预测直接支持频谱感知RAN功能，如动态载波激活、拥堵避免和主动频谱共享，使所提出的框架适用于动态频谱接入场景。
英文摘要：
Accurate forecasting of residual Physical Resource Blocks (PRBs) is critical for proactive network slice provisioning, energy-efficient operation, and spectrum-aware decision making in cellular systems, where residual PRBs serve as a practical proxy for short- and medium-term spectrum availability. Existing PRB prediction methods typically rely only on historical PRB values and are trained independently per carrier or sector, limiting their ability to capture cross-carrier dependencies and providing no measure of forecast uncertainty. Moreover, point forecasts alone are insufficient for robust spectrum-aware control under highly variable traffic conditions. This paper proposes PRB-RUPFormer, a recursive unified probabilistic Transformer for residual PRB forecasting. The proposed model jointly processes multivariate KPI time series using temporal, seasonal, and carrier-aware embeddings, preserving inter-metric temporal coupling during recursive rollout and stabilizing long-horizon forecasting. A single shared model is trained across all carriers and sectors of an eNB, enabling efficient learning of joint traffic dynamics with low computational overhead. Forecast uncertainty is captured through quantile-based prediction intervals, providing confidence-aware estimates of future PRB availability. Evaluations on six months of commercial LTE network data from multiple U.S. locations demonstrate median MAE below 0.05 and hit probabilities above 0.80 for both one-day and seven-day recursive forecasts. These probabilistic predictions directly support spectrum-aware RAN functions such as dynamic carrier activation, congestion avoidance, and proactive spectrum sharing, making the proposed framework well-suited for dynamic spectrum access scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
