261 research outputs found
Measurement-based modeling of RF-EMF exposure in urban environments using artificial intelligence techniques
Assessment of contribution of other users to own total whole-body RF absorption in train environment
Assessment of long-term spatio-temporal radiofrequency electromagnetic field exposure
As both the environment and telecommunications networks are inherently dynamic, our exposure to environmental radiofrequency (RF) electromagnetic fields (EMF) at an arbitrary location is not at all constant in time. In this study, more than a year's worth of measurement data collected in a fixed low-cost exposimeter network distributed over an urban environment was analysed and used to build, for the first time, a full spatio-temporal surrogate model of outdoor exposure to downlink Global System for Mobile Communications (GSM) and Universal Mobile Telecommunications System (UMTS) signals. Though no global trend was discovered over the measuring period, the difference in measured exposure between two instances could reach up to 42 dB (a factor 12,000 in power density). Furthermore, it was found that, taking into account the hour and day of the measurement, the accuracy of the surrogate model in the area under study was improved by up to 50% compared to models that neglect the daily temporal variability of the RF signals. However, further study is required to assess the extent to which the results obtained in the considered environment can be extrapolated to other geographic locations
RF-EMF exposure induced by mobile phones operating in LTE small cells in two different urban cities
With the huge growth in data traffic, the densification of the macro cell (MC) layer with low-powered small cell (SC) base stations (resulting in a heterogeneous network) will improve network performances in terms of radio coverage and capacity. However, this may influence the human exposure to radiofrequency electromagnetic fields (RF-EMFs). Through measurement campaigns in two different urban cities (in France and the Netherlands), the authors characterized the RF-EMF exposure induced by LTE (Long-Term Evolution) MC and SC networks, while considering radio emissions from both base stations (downlink or DL) and user equipment (uplink or UL). For an internet data usage and with respect to an MC connection, results showed that an SC connection may increase the DL exposure while decreasing the UL exposure (with a factor of 5 to 17), mainly due to the lower mobile phone emitted power and depending on whether the throughput is limited or not. Furthermore, the city with a dense network is characterized by low UL exposure and high DL exposure
Discrepancies of measured SAR between traditional and fast measuring systems
Human exposure to mobile devices is traditionally measured by a system in
which the human body (or head) is modelled by a phantom and the energy absorbed
from the device is estimated based on the electric fields measured with a
single probe. Such a system suffers from low efficiency due to repeated
volumetric scanning within the phantom needed to capture the absorbed energy
throughout the volume. To speed up the measurement, fast SAR (specific
absorption rate) measuring systems have been developed. However, discrepancies
of measured results are observed between traditional and fast measuring
systems. In this paper, the discrepancies in terms of post-processing
procedures after the measurement of electric field (or its amplitude) are
investigated. Here, the concerned fast measuring system estimates SAR based on
the reconstructed field of the region of interest while the amplitude and phase
of electric field are measured on a single plane with a probe array. The
numerical results presented indicate that the fast SAR measuring system has the
potential to yield more accurate estimations than the traditional system, but
no conclusion can be made on which kind of system is superior without knowledge
of the field-reconstruction algorithms and the emitting source
Use of artificial intelligence to model exposure to radiofrequency electromagnetic fields based on sensor network measurements
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