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/compliance assessment study collected 5- and 30-minute transmitted power data over one month from more than 400 5G FR2 beamforming base stations in urban Japan to evaluate the IEC 62232:2025 “actual maximum approach.” All measured transmitted powers were below configured maxima, with 30-minute averaged normalized maxima reported as < -8 dB, while 5-minute averaged maxima reached about -3 dB under low UE counts. The authors report that the actual maximum approach can avoid overestimation for longer averaging times, but may underestimate exposure for stations with three or fewer UEs if power is not monitored and controlled.

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 exposure assessment reports 400 ground-level electric field measurements in Greek urban and suburban areas during 5G deployment. It finds that 4G contributes most to overall measured EMF exposure, while 5G currently contributes less. The study reports an inverse relationship between 3.5 GHz EMF levels and distance from 5G base stations, with urban areas showing higher levels than suburban areas.

This protocol and pilot study evaluated whether smartphone signal strength indicators can be converted into RF-EMF exposure estimates using derived formulas and regression models. The study reports a positive log-linear association between LTE RSSI and far-field (base station) exposure aggregated by location, while handset-related exposure at the ear and chest during data transmission showed negative log-linear trends with improving signal quality. The authors conclude the ETAIN 5G-Scientist app may support large-scale RF-EMF exposure assessment, but emphasize the need for more data to improve accuracy and address uncertainties in individual measurements.

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.