Modeling and Characterization of the Uplink and Downlink Exposure in Wireless Networks

Abstract

This paper deals with a new methodology to assess the exposure induced by both uplink and downlink of a cellular network using 3D electromagnetic simulations. It aims to analyze together the exposure induced by a personal device (uplink exposure) and that induced by a base station (downlink exposure). The study involved the major parameters contributing to variability and uncertainty in exposure assessment, such as the user's posture, the type of wireless device, and the propagation environment. Our approach is relying basically on the modeling of the power radiated by the personal device and the ambient electric field, while taking into account the effects of human body shadowing and the propagation channel fluctuations. The exposure assessment as well as the human-wave interactions has been simulated using the finite difference in time domain method (FDTD). In uplink scenarios, four FDTD simulations were performed with a child model, used in two postures (sitting and standing) and in two usage scenarios (voice and data), which aimed to examine the exposure induced by a mobile phone and a tablet emitting, respectively, at 900 MHz and 1940 MHz. In the downlink scenario, a series of FDTD simulations of an exposure to a single plane wave and multiplane waves have been conducted, and an efficient metamodeling of the exposure using the Polynomial Chaos approach has been developed.

Mobile phones: A trade-off between speech intelligibility and exposure to noise levels and to radio-frequency electromagnetic fields

“…The full phantom was included in the simulations in anatomical upright position. Krayni et al (2017) demonstrated previously that phone efficiency and induced SAR values show little or no difference between upright and sitting phantom posture. The dipoles are fed a harmonic, sinusoidal wave of amplitude 1 V at 50 Ohms at 897 MHz and 1800 MHz, respectively.…”

“…We find similar differences between SAR brain obtained for the 'at ear' configuration at 897 MHz and SAR brain in the other two configurations at 1800 MHz. Krayni et al (2017) found no difference in whole-body average SAR and radiation efficiency of the phone in sitting and standing positions. This justifies our choice to numerically model the phantom in upright position.…”

“…The TX power in the 'At Ear' configuration is higher, so even in the unlikely event that the normalized, simulated SAR brain values would be the same for the "At Ear" and 'Headset' configurations, the effective SAR brain values (normalized to the measured TX powers) would still be lower in the 'Headset' configuration. Krayni et al (2017) found higher whole-body averaged SARs normalized to an output power of 1 W for a mobile phone emitting at 900 MHz at the ear in comparison to a tablet emitting at 1.9 GHz in front of the body. We find similar differences between SAR brain obtained for the 'at ear' configuration at 897 MHz and SAR brain in the other two configurations at 1800 MHz.…”

“…However, this data is not usable for cellular technologies. Additionally, published TX data contain no behavioral information of the user, which can have a large influence on the induced SAR (Krayni et al, 2016(Krayni et al, , 2017. A previously proposed technique (Wiart et al, 2000;Hillert, et al, 2006;Morrissey, 2007;Vrijheid et al, 2009;Kelsh et al, 2011;Kühn and Kuster, 2013a; suggests the usage of application software on the smartphone or software modification on the smartphone in order to register TX powers during phone calls.…”

IEEE Committee on Man and Radiation—COMAR Technical Information Statement: Health and Safety Issues Concerning Exposure of the General Public to Electromagnetic Energy from 5G Wireless Communications Networks

“…Since in most cases the stronger exposure to cellphone users is from uplink signals from their own devices, the presence of small cells will generally reduce overall RF exposure to a user or a bystander ( Stephan et al 2014 ; Mazloum et al 2017 ). However, many variables determine personal exposure to RF fields, and simple generalizations of this sort do not always apply ( Lonn et al 2004 ; Boursianis et al 2012 ; Durrenberger et al 2014 ; Plets et al 2015 ; Krayni et al 2017 ; Huang 2018 ; Zeleke et al 2018 ).…”