This paper reports results from a large-scale, multi-phase measurement campaign in mainland France assessing changes in RF exposure associated with 5G deployment from 2020 to 2024. Using more than 24,000 on-ground measurements in direct view of 5G antennas, it finds small average increases in broadband exposure and increased contributions from 5G-related bands over time. The 3.5 GHz band contribution increased but remained a secondary contributor compared with legacy 800/900 MHz bands, and exposure during active downloading was higher than in idle mode.

This exposure-assessment study proposes and validates a method to measure instantaneous RF exposure under typical base station load by generating defined data rates (low/medium/high) using iPerf and measuring channel power across services. Validation at four base stations suggests the approach is reliable across different times of day and loads, with reproducible results when averaging over 30 sweeps. Comparisons indicate iPerf-provoked constant data rates generally match exposure during real application usage, with few deviations beyond stated uncertainty.

This exposure assessment study analyzed smartphone-logged 4G LTE RSSI and GPS data from adults in France to identify determinants of downlink signal strength. RSSI varied with geospatial factors (distance to antennas, antenna density, urbanicity) and time of day, and was also influenced by technical smartphone parameters. The study reports an estimated electric field strength derived from RSSI, but notes high uncertainty in this conversion.

This exposure-assessment study compares RF-EMF exposure maps produced using a broadband meter versus a personal exposimeter and aims to correct personal exposimeter readings to match broadband-based maps. The authors report that LOS/NLOS-specific correction factors reduce discrepancies, particularly improving LOS measurements affected by body shielding. A genetic algorithm is used to optimize correction factors and support scalable urban exposure mapping, with the authors noting that additional validation in other environments is needed.

This paper presents a machine-learning method to estimate ground-level electromagnetic radiation (electric field strength) in the near field of 5G base stations, using multiple technical and environmental input parameters. The authors report experimental performance with a mean absolute percentage error of about 5.89% and suggest the approach can reduce costs compared with on-site measurements. The work is positioned as supporting exposure management and base-station placement, while noting the need for careful EMF management due to potential health-risk links.

This conference paper examines extrapolation factors used for in-situ EMF exposure assessment of 5G base stations in realistic indoor and outdoor scenarios. Using both frequency-selective and code-selective measurement approaches under varying traffic conditions, it reports substantial variability in extrapolated exposure estimates driven largely by antenna radiation patterns. Outdoor environments showed more stable extrapolation than indoor environments, highlighting challenges for reliable exposure assessment when antenna patterns and network configurations are not well characterized.

This longitudinal cohort study examined the temporal relationship between somatization and perceived RF-EMF exposure, comparing the attribution hypothesis with the nocebo hypothesis. Using AMIGO questionnaire data from 2011 and 2015, regression analyses suggested the attribution hypothesis more often explained symptom reporting linked to perceived base station RF-EMF exposure and perceived electricity exposure than the nocebo hypothesis. The authors state this contrasts with prior literature and note that a nocebo effect is not fully excluded.