EMFusion: Conditional Diffusion Framework for Trustworthy Frequency Selective EMF Forecasting in Wireless Networks

2025-12-16

AI Summary

Accurate spectral prediction of electromagnetic field (EMF) exposure under multi-operator, multi-band coordination remains challenging; conventional single-variable broadband models fail to capture frequency selectivity and cross-operator heterogeneity. Method: We formulate EMF prediction as a structured time-series imputation task and propose a conditional diffusion framework integrating residual U-Net architecture and cross-attention mechanisms, jointly leveraging masked imputation sampling and multi-source contextual embeddings (e.g., operational time windows) to enable explicit uncertainty quantification and dynamic context-aware multivariate probabilistic forecasting. Results: Evaluated on a real-world frequency-selective EMF dataset, our method reduces CRPS, normalized RMSE, and uncertainty error by 23.85%, 13.93%, and 22.47%, respectively—outperforming state-of-the-art approaches. The framework delivers interpretable, high-fidelity spectral awareness critical for regulatory compliance assessment and health risk early warning.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Relational Probabilistic Models
• Computer Vision: Diffusion Models for Vision

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Web Mining and Content Analysis: Large pretrained models with web data

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%.

Problem

• Forecasts frequency-selective EMF levels for wireless network planning
• Integrates contextual factors and quantifies uncertainty for trustworthy decisions
• Handles irregular measurements via imputation-based structural inpainting approach

Innovation

• Conditional diffusion framework for frequency-selective EMF forecasting
• Residual U-Net with cross-attention integrates contextual factors
• Imputation-based sampling ensures temporal coherence in predictions