Advancing EMF Compliance for 5G Massive MIMO Antennas: From 2-D Time-Averaged Models to Realistic 3-D Spatial–Temporal Representations

The deployment of 5G networks, leveraging technologies like massive MIMO and beamforming, has transformed signal propagation by introducing dynamic and directional transmission patterns. While these advances improve spectral efficiency, the complexity of evaluating exposure to Radio Frequency Electromagnetic Field (RF-EMF) increased, rendering traditional compliance methods overly conservative. To address this, recent international standards on RF-EMF calculations and measurements have introduced statistical approaches that better reflect real-world operating conditions by accounting for temporal variability in traffic load and beam patterns. This paper presents a novel extension to the effective maximum approach defined by IEC (International Electrotechnical Committee) by incorporating spatial dynamics derived from real-time base station measurements. These measurements are stored in counters that capture the three-dimensional (3D) power distribution of transmitted signals, enabling a more accurate representation of actual operating conditions. Two innovative methodologies are proposed (semi-vectorial and vectorial), both resulting in a more realistic estimation of RF-EMF exposure with respect to currently used methodologies. Each method uses different power reduction factors to reflect how signals change over time and space. Unlike existing models, the two new approaches capture the full spatio-temporal complexity of 5G emissions, offering a first-of-its-kind framework for realistic RF-EMF exposure assessment. A real-world case study demonstrates that this methodology significantly reduces conservatism in compliance boundaries, without compromising safety. These insights are particularly valuable for regulators and policymakers, enabling more accurate and proportionate exposure assessment methodologies that support the safe and efficient deployment of 5G and future wireless technologies.

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Publication Details

Journal
IEEE Open Journal of Antennas and Propagation
Published
2025-12-09
DOI
https://doi.org/10.1109/ojap.2025.3642188
Primary Topic
Electromagnetic Fields and Biological Effects
Type
article
Field-Weighted Citation Impact
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article

Advancing EMF Compliance for 5G Massive MIMO Antennas: From 2-D Time-Averaged Models to Realistic 3-D Spatial–Temporal Representations

Riccardo Suman, Simona Valbonesi, Andrea Garzia, Stefano D’Elia et al.
IEEE Open Journal of Antennas and Propagation
Electromagnetic Fields and Biological Effects
article

Advancing EMF Compliance for 5G Massive MIMO Antennas: From 2-D Time-Averaged Models to Realistic 3-D Spatial–Temporal Representations

Riccardo Suman, Simona Valbonesi, Andrea Garzia, Stefano D’Elia, Paolo Grazioso
article en

Abstract

The deployment of 5G networks, leveraging technologies like massive MIMO and beamforming, has transformed signal propagation by introducing dynamic and directional transmission patterns. While these advances improve spectral efficiency, the complexity of evaluating exposure to Radio Frequency Electromagnetic Field (RF-EMF) increased, rendering traditional compliance methods overly conservative. To address this, recent international standards on RF-EMF calculations and measurements have introduced statistical approaches that better reflect real-world operating conditions by accounting for temporal variability in traffic load and beam patterns. This paper presents a novel extension to the effective maximum approach defined by IEC (International Electrotechnical Committee) by incorporating spatial dynamics derived from real-time base station measurements. These measurements are stored in counters that capture the three-dimensional (3D) power distribution of transmitted signals, enabling a more accurate representation of actual operating conditions. Two innovative methodologies are proposed (semi-vectorial and vectorial), both resulting in a more realistic estimation of RF-EMF exposure with respect to currently used methodologies. Each method uses different power reduction factors to reflect how signals change over time and space. Unlike existing models, the two new approaches capture the full spatio-temporal complexity of 5G emissions, offering a first-of-its-kind framework for realistic RF-EMF exposure assessment. A real-world case study demonstrates that this methodology significantly reduces conservatism in compliance boundaries, without compromising safety. These insights are particularly valuable for regulators and policymakers, enabling more accurate and proportionate exposure assessment methodologies that support the safe and efficient deployment of 5G and future wireless technologies.

IEEE Open Journal of Antennas and PropagationVol. 7(1)
Fondazione "Ugo Bordoni" (IT), Vodafone (Italy) (IT)
Openalex Percentile: Top 73%
Electromagnetic Fields and Biological Effects
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