A comprehensive review of 5G NR RF-EMF exposure assessment technologies
fundamentals, advancements, challenges, niches, and implications
Erdal Korkmaz (TU Delft - Electrical Engineering, Mathematics and Computer Science, De Haagse Hogeschool)
Sam Aerts (De Haagse Hogeschool)
Richard Coesoij (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Chhavi Raj Bhatt (Australian Radiation Protection and Nuclear Safety Agency)
Maarten Velghe (Rijksinstituut voor Volksgezondheid en Milieu)
Loek Colussi (Dutch Authority for Digital Infrastructure)
Derek Land (De Haagse Hogeschool)
Nikolaos Petroulakis (Institute of Computer Science)
Marco Spirito (TU Delft - Electrical Engineering, Mathematics and Computer Science)
John Bolte (De Haagse Hogeschool, Rijksinstituut voor Volksgezondheid en Milieu)
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Abstract
This review offers a detailed examination of the current landscape of radio frequency (RF) electromagnetic field (EMF) assessment tools, ranging from spectrum analyzers and broadband field meters to area monitors and custom-built devices. The discussion encompasses both standardized and non-standardized measurement protocols, shedding light on the various methods employed in this domain. Furthermore, the review highlights the prevalent use of mobile apps for characterizing 5G NR radio network data. A growing need for low-cost measurement devices is observed, commonly referred to as “sensors” or “sensor nodes”, that are capable of enduring diverse environmental conditions. These sensors play a crucial role in both microenvironmental surveys and individual exposures, enabling stationary, mobile, and personal exposure assessments based on body-worn sensors, across wider geographical areas. This review revealed a notable need for cost-effective and long-lasting sensors, whether for individual exposure assessments, mobile (vehicle-integrated) measurements, or incorporation into distributed sensor networks. However, there is a lack of comprehensive information on existing custom-developed RF-EMF measurement tools, especially in terms of measuring uncertainty. Additionally, there is a need for real-time, fast-sampling solutions to understand the highly irregular temporal variations EMF distribution in next-generation networks. Given the diversity of tools and methods, a comprehensive comparison is crucial to determine the necessary statistical tools for aggregating the available measurement data.