Abstract
The rapid growth in wireless infrastructure has increased the need to accurately estimate and forecast electromagnetic field (EMF) levels to ensure ongoing compliance, assess potential health impacts, and support efficient network planning. While existing studies rely on univariate forecasting of wideband aggregate EMF data, frequency-selective multivariate forecasting is needed to capture the inter-operator and inter-frequency variations essential for proactive network planning. To this end, this paper introduces EMFusion, a conditional multivariate diffusion-based probabilistic forecasting framework that integrates diverse contextual factors (e.g., time of day, season, and holidays) while providing explicit uncertainty estimates. The proposed architecture features a residual U-Net backbone enhanced by a cross-attention mechanism that dynamically integrates external conditions to guide the generation process. Furthermore, EMFusion integrates an imputation-based sampling strategy that treats forecasting as a structural inpainting task, ensuring temporal coherence even with irregular measurements. Unlike standard point forecasters, EMFusion generates calibrated probabilistic prediction intervals directly from the learned conditional distribution, providing explicit uncertainty quantification essential for trustworthy decision-making. Numerical experiments conducted on frequency-selective EMF datasets demonstrate that EMFusion with the contextual information of working hours outperforms the baseline models with or without conditions. The EMFusion outperforms the best baseline by 23.85% in continuous ranked probability score (CRPS), 13.93% in normalized root mean square error, and reduces prediction CRPS error by 22.47%.
Abstract (translated)
无线基础设施的快速增长增加了对准确估计和预测电磁场（EMF）水平的需求，以确保持续合规性、评估潜在健康影响和支持高效网络规划。尽管现有研究依赖于宽带聚合EMF数据的单变量预测，但为了捕捉运营商间和频段间的变动情况——这些对于主动网络规划至关重要——需要进行频率选择性的多变量预测。 为此，本文介绍了EMFusion，这是一种基于条件多变量扩散的概率预测框架，它结合了多样化的上下文因素（如一天中的时间、季节和假日），同时提供明确的不确定性估计。所提出的架构以残差U-Net骨干为基础，并通过交叉注意机制增强了外部条件的动态整合，以引导生成过程。此外，EMFusion集成了基于插补的采样策略，将预测视为结构修复任务，即使在不规则测量的情况下也能确保时间一致性。与标准点预测器不同的是，EMFusion直接从学习到的条件分布中生成校准的概率预测区间，提供了进行信任决策所需的具体不确定性量化。 在频率选择性EMF数据集上进行的数值实验表明，在结合工作时间上下文信息后，EMFusion的表现优于有或无条件下的基线模型。与最佳基准相比，EMFusion在连续排名概率评分（CRPS）中提高了23.85%，在归一化均方根误差中提高了13.93%，将预测CRPS误差减少了22.47%。
URL
https://arxiv.org/abs/2512.15067
