Statistical Characterization and Modeling of Indoor RF-EMF Down-Link Exposure

Abstract

With the increasing use of wireless communication systems, assessment of exposure to radio-frequency electromagnetic field (RF-EMF) has now become very important due to the rise of public risk perception. Since people spend more than 70% of their daily time in indoor environments, including home, office, and car, the efforts devoted to indoor RF-EMF exposure assessment has also increased. However, assessment of indoor exposure to RF-EMF using a deterministic approach is challenging and time consuming task as it is affected by uncertainties due to the complexity of the indoor environment and furniture structure, existence of multiple reflection, refraction, diffraction and scattering, temporal variability of exposure, and existence of many obstructions with unknown dielectric properties. Moreover, it is also affected by the existence of uncontrolled factors that can influence the indoor RF-EMF exposure such as the constant movement of people and random movement of furniture and doors as people are working in the building. In this study, a statistical approach is utilized to characterize and model the total indoor RF-EMF down-link (DL) exposure from all cellular bands on each floor over the length of a wing since the significance of distance is very low between any two points on each floor in a wing and the variation of RF-EMF DL exposure is mainly influenced by the local indoor environment. Measurements were conducted in three buildings that are located within a few hundred meters vicinity of two base station sites supporting several cellular technologies (2G, 3G, 4G, and 5G). We apply the one-sample Kolmogorov–Smirnov test on the measurement data, and we prove that the indoor RF-EMF DL exposure on each floor over the length of a wing is a random process governed by a Gaussian distribution. We validate this proposition using leave-one-out cross validation technique. Consequently, we conclude that the indoor RF-EMF DL exposure on each floor over the length of a wing can be modeled by a Gaussian distribution and, therefore, can be characterized by the mean and the standard deviation parameters.

Assessment of Electromagnetic Field Exposure on European Roads: A Comprehensive In Situ Measurement Campaign

“…With the comparison of existing measurement campaigns [7][8][9][10][12][13][14][15][18][19][20][21]39], we present results for the E-field in a complex environment due to the semi-resonant nature of the vehicle body. The presented information for the field strength of Ex, Ey, and Ez components serves as an indication for the orientation of the electric field, which is valuable for the design of both new absorbers of EMF for the car cabin and new flexible rectennas for EMF energy harvesting in vehicles.…”

“…The growth development of 5G cellular networks is also associated with the widespread deployment of base stations [7]. Along with this growth, questions also arise about the potential adverse effects on human health due to electromagnetic fields (EMFs) from base station antennas [8][9][10]. To debate this question, the European Parliament (Scientific and Technology Options Assessment Panel) organized a workshop on "Health and Environmental Impacts of 5G" [11].…”

“…To debate this question, the European Parliament (Scientific and Technology Options Assessment Panel) organized a workshop on "Health and Environmental Impacts of 5G" [11]. Several papers have also focused on EMF assessment in various microenvironments, such as indoor [9,12,13] and outdoor (rural and urban) environments [8,[14][15][16]. The data from the five largest monitoring networks in five different European countries show that exposure is below the International Commission on Non-Ionizing Radiation Protection (ICNIRP) reference level [17], and the median electric field value in the different countries varies between 0.67 V/m and 1.51 V/m [8].…”

On the Quasistationarity of the Ambient Electromagnetic Field Generated by Wi-Fi Sources

“…Under various conceptual umbrellas like "smart appliances", "Internet of Things", or "Industry 4.0", wireless communication is increasingly integrated into various facets of daily life, providing a more convenient alternative for data utilization and prompting changes in telecommunications technology. These changes increasingly involve encoding information into the RF spectrum rather than increasing the transmission power [4,5].…”

SDR-Based Portable System for Evaluating Exposure to Ambient Electromagnetic Fields

“…Telecommunications engineers strive to achieve comprehensive network coverage, including indoor areas, but this objective leads to the quasi-permanent presence of low-intensity RF-EMF indoors. Characterizing exposure levels in indoor environments proves to be a more straightforward task, primarily relying on the utilization of fixed laboratory equipment powered by an electrical network [3,8,16,17].…”