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In this paper, the electromagnetic environment (EME) characteristics on Wangjiang campus of Sichuan University, a typical densely populated urban area, are measured and analyzed. According to the people's daily routine, the EME measurements are performed during daytime, nighttime, weekday, and weekend, respectively. By measuring the electric field...
Citations
... With the development of mobile communication technology and the popularization of smartphones, electromagnetic exposure due to mobile communications is receiving increasing attention (Balmori, 2022;Jagetia, 2022;Kaur et al., 2023;Tran et al., 2023). Moreover, studies over the years have shown that electromagnetic exposure caused by downlink in mobile communication occupies a significant proportion, so effective measurement of exposure to downlink signals has always been the focus in the field of electromagnetic measurement Choi et al., 2018;Eeftens et al., 2018;Koppel et al., 2022;Zhao et al., 2019). ...
Electromagnetic exposure caused by mobile communication signals has always been a cause of concern. Due to the cost and inconvenience of professional measurement equipment, researchers have turned to smartphone APPs to study and assess the electric field strength caused by mobile communication signals. However, existing cell phone‐based measurements have two weaknesses. First, no system architecture suitable for large‐scale crowdsourced testing has been proposed. Second, since smartphone sensors cannot measure electric field strength directly, existing methods for converting the received signal power of the phone and electric field strength have errors of more than 5 dB. This paper proposes a measurement and calibration method for electric field strength of mobile communication signals based on a smartphone app and gradient boosting decision tree (GBDT). This method consists of a downlink signal acquisition system based on an APP and a calibration model based on GBDT to convert received signal power into electric field strength. The experimental results show that the proposed model achieves a R² score of 0.93 and a MAE of 0.97 dB. Compared with the existing methods, our method improves the calibration accuracy by 4 dB, enabling large‐scale, low‐cost, and high‐precision direct measurement of the electric field strength of mobile communication signals.
... On the one hand, the electromagnetic radiation of anthropogenic origin above certain intensity levels is biologically active and can cause a number of harmful effects on human health. These issues are most clearly characterized by the concept of radio-wave ecology (environmental impact of television and radio broadcast) [1] - [5]. On the other hand an uncontrolled increase in electromagnetic emissions results in an increase in background noise, which above a certain level can deteriorate the quality of connections and even make parts of the frequency spectrum unusable. ...
The current study was conducted by making a series of experimental measurements of the electric field strength E. The obtained results have been interpreted using the method of mathematical modelling because the object of study – electromagnetic environment (EME) is a multifactorial system. With the accepted limitation of considering the influence of only two factors (frequency and time intervals) a two-factor regression analysis was used. The processing of the obtained data was performed by two-dimensional quadratic objective function.
... Therefore, these characteristics of the study area are the main motivations for conducting this research. For example, Zhao et al. (2019) conducted a study on a university campus. In another study (Karadağ, 2019), measurements of electromagnetic pollution were carried out in a hospital. ...
Exposure to radiofrequency electromagnetic fields (RF-EMFs) is considered an area of significant importance in the medical and scientific community. However, the availability of exposure data for indoor and outdoor locations in universities is limited and currently inconsiderate in Latin America. The aim of this work was to evaluate the electric field levels due to mobile telecommunication technologies and Wi-Fi to which students and faculty staff from two campuses of a higher education institution are exposed. Using a portable spectrum analyzer, we carried out 516 short-term measurements in the 800–3000 MHz frequency range at both indoor and outdoor locations. These locations were chosen to cover all areas of the assessed buildings. The electric field differences between floors and buildings are discussed. Finally, we compared the electric field levels with exposure limits. The highest electric field level measured was 13.97 V/m at the 850 MHz band. However, the average electric field values were below 2 V/m. The greatest contribution to the total electric field was due to sources using the 850 MHz and 1900 MHz bands (98%), while the contribution of the Wi-Fi network was low (1.0%). The results show that all the electric field levels measured were lower than the ICNIRP reference levels for radio-frequency exposure.
Indoor wideband electromagnetic radiation (EMR) over seven days and potential radiation sources were measured and analyzed in this paper. The research results show that Wi-Fi and base station equipment are the main factors causing indoor electromagnetic exposure, and the radiation intensity of the former is much higher than the latter when the equipment is working. Also, the change of EMR over time conforms to the users’ daily work and rest cycle and contains a significant low-frequency component with a 24-hour cycle and complex high-frequency components. Besides, the intensive use of electronic products by users has been proved to have a great impact on the frequency characteristics of the electric field, and the complexity of the time-frequency characteristics of EMR time series has increased, resulting in poor prediction results of the models such as the seasonal autoregressive moving average model (SARIMA) and the long short-term memory neural network (LSTM). To this end, a prediction scheme combining wavelet transform and time-series analysis methods is proposed in this paper. To verify the effectiveness of the proposed hybrid method, the samples of three days are exploited to perform a loop test of the prediction accuracy under different prediction steps. Besides, the results are compared with those obtained by single SARIMA and LSTM. The experimental results show that the proposed hybrid prediction method effectively predicts electric field radiation with complex frequency characteristics and significantly outperforms other methods in terms of prediction accuracy and prediction length.
