Radiofrequency Exposure Levels from Mobile Phone Base Stations in Outdoor Environments and an Underground Shopping Mall in Japan

Onishi, Teruo; Ikuyo, Miwa; Tobita, Kazuhiro; Liu, Sen; Taki, Masao; Watanabe, Soichi

doi:10.3390/ijerph18158068

Cited by 19 publications

(22 citation statements)

References 18 publications

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“…For example, Table 1 shows the average power density of different frequency bands that are commonly used for mobile communication, radio or TV broadcasting, and wireless network [17][18][19]. The mean value of RF energy density in several countries [20][21][22][23][24] with different ranges of average cellular subscription has been plotted in Figure 2 to provide an overview of RF EH feasibility. Theoretically, countries with higher average cellular subscription values tend to have a higher RF energy density due to the higher amount of call traffic and signal transmission.…”

Section: Background Researchmentioning

confidence: 99%

Low Voltage Switched-Capacitive-Based Reconfigurable Charge Pumps for Energy Harvesting Systems: An Overview

Ramiah

Churchill

et al. 2022

IEEE Access

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Conventional SC-based charge pump with fixed conversion ratio is predominantly adopted in energy harvesting (EH) systems, which becomes a bottleneck for attaining high power conversion efficiency (PCE) with wide dynamic range (WDR). This article reviews and explores the SC-based reconfigurable charge pump (RCP) as an alternative voltage-boosting element for the EH system. An overview of the RCP structure is established, followed by a comprehensive review of CP reconfigurable topologies along with state-of-art architecture. Design consideration of the RCP in terms of dynamic range, PCE, and VCE is provided to give a clear insight into the future development of RCP architecture for the EH system.INDEX TERMS Energy harvesting (EH), reconfigurable charge pump, wide dynamic range, capacitivebased converter, dc-dc converter, CMOS

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Section: Background Researchmentioning

confidence: 99%

Low Voltage Switched-Capacitive-Based Reconfigurable Charge Pumps for Energy Harvesting Systems: An Overview

Ramiah

Churchill

et al. 2022

IEEE Access

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“…[21]. Aiming to assess personal exposure to RF-EMF at such points, scientists have utilized environmental and mainly personal dosimeters (or exposimeters) in schools [22], [23], [24], [25], [26], [27], universities [28], hospitals [29], playgrounds, offices and malls [30], [31], [32]. However, this approach requires manual measurements, often cumbersome and costly equipment, while it doesn't support continuous and largescale monitoring.…”

Section: Introductionmentioning

confidence: 99%

RF-EMF Exposure Assessments in Greek Schools to Support Ubiquitous IoT-Based Monitoring in Smart Cities

et al. 2023

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Everyday living environments concentrate a growing amount of wireless communications leading to increased public concern for radiofrequency (RF) electromagnetic fields (EMF) exposure. Recent technological advances are turning the focus on Internet of Things (IoT) systems that enable automated and continuous real-time EMF monitoring, facing however several challenges mainly stemming from infrastructural costs. This paper seeks to provide a comprehensive view of RF-EMF levels in Greece and evidence-based decision support for a spatially prioritized deployment of an IoT RF-EMF monitoring system. We applied the stratified sampling method to estimate Electric Field Strength (EFS) in the 27MHz-3GHz range in 661 schools. Three different residential areas were considered, i.e. urban, semi-urban and rural. Results showed that the 95% confidence interval for the EFS is (0.40, 0.44) with central value equal to the sample mean 0.42 V/m. We obtained strong evidence that the mean EFS value for all Greek schools is 0.42, which is 52 times lower than the Greek safety limit and equal to 1% of international limits. Mean EFS values of individual residential areas were also significantly below safety limits. Rural areas displayed the highest EFS peaks comprising the strongest candidate to start the deployment of an IoT RF-EMF monitoring system from.INDEX TERMS Radiofrequency electromagnetic fields (RF-EMF), RF-EMF exposure in schools, Internet of Things, RF-EMF monitoring, smart cities.

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“…In [4], the overall impact of the densification of macro cells with small cells on global human exposure (including both DL and UL) was studied by using two drive test solutions. In [6], RF-EMF exposure induced by RF base station (BS) antennas was assessed in an outdoor environment and an underground shopping mall in Japan. The measurement protocols are also being updated with the deployment of new 5G networks, as proposed in [7,8].…”

Section: Introductionmentioning

confidence: 99%

Prediction of RF-EMF Exposure by Outdoor Drive Test Measurements

Wang

Mazloum

Wiart

2022

Telecom

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In this paper, we exploit the artificial neural network (ANN) model for a spatial reconstruction of radio-frequency (RF) electromagnetic field (EMF) exposure in an outdoor urban environment. To this end, we have carried out a drive test measurement campaign covering a large part of Paris, along a route of approximately 65 Km. The electric (E) field strength has been recorded over a wide band ranging from 700 to 2700 MHz. From these measurement data, the E-field strength is extracted and computed for each frequency band of each telecommunication operator. First, the correlation between the E-fields at different frequency bands is computed and analyzed. The results show that a strong correlation of E-field levels is observed for bands belonging to the same operator. Then, we build ANN models with input data encompassing information related to distances to N neighboring base stations (BS), receiver location and time variation. We consider two different models. The first one is a fully connected ANN model, where we take into account the N nearest BSs ignoring the corresponding operator. The second one is a hybrid model, where we consider locally connected blocks with the N nearest BSs for each operator, followed by fully connected layers. The results show that the hybrid model achieves better performance than the fully connected one. Among N∈{3,5,7}, we found out that with N=3, the proposed hybrid model allows a good prediction of the exposure level while the maintaining acceptable complexity of the model.

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Radiofrequency Exposure Levels from Mobile Phone Base Stations in Outdoor Environments and an Underground Shopping Mall in Japan

Cited by 19 publications

References 18 publications

Low Voltage Switched-Capacitive-Based Reconfigurable Charge Pumps for Energy Harvesting Systems: An Overview

Low Voltage Switched-Capacitive-Based Reconfigurable Charge Pumps for Energy Harvesting Systems: An Overview

RF-EMF Exposure Assessments in Greek Schools to Support Ubiquitous IoT-Based Monitoring in Smart Cities

Prediction of RF-EMF Exposure by Outdoor Drive Test Measurements

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