Time-Series-Based Model and Validation for Prediction of Exposure to Wideband Radio Frequency Electromagnetic Radiation

DeLellis, Pietro; Iudice, Francesco Lo; Pasquino, Nicola

doi:10.1109/tim.2019.2927649

Cited by 13 publications

(4 citation statements)

References 26 publications

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“…A weekly (Fig. 2b) pattern shows in all time series [32], where weekdays usually reach higher peaks than weekends, although the difference is more relevant in some locations than in others: see, for example, the apparent variations at location 1 (Rome, Department of Electronic Engineering) as opposed to location 3 (Turin, Polytechnic of Turin). Fig.…”

Section: A Time Seriesmentioning

confidence: 98%

High Noon for Mobile Networks: Short-Time EMF Measurements to Capture Daily Exposure

Adda,

Chiaraviglio,

Franci

et al. 2023

IEEE Trans. Instrum. Meas.

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Exposure from radio base stations is often only monitored over short periods, typically during the daily hours of working days. However, base station exposure can vary throughout the day and even between working days and weekends or holidays. The objective of this study is to determine to what extent short-term exposure measurements taken during daily hours are representative of daily average exposure levels. To achieve this goal, we analyzed a set of long-term measurements taken by monitoring units located in sensitive areas, which are characterized by a homogeneous distribution of users over working hours, such as hospitals, train stations, and university centers. Our results reveal that 6-minute measurements taken on working days can overestimate the average exposure level over 24 hours if taken over a wider time interval than that commonly considered for peak traffic and, therefore, higher exposure. Based on the common pattern of exposure over time in various locations, an extrapolation factor is proposed to predict daily exposure levels from short-time measurements.

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Section: A Time Seriesmentioning

confidence: 98%

High Noon for Mobile Networks: Short-Time EMF Measurements to Capture Daily Exposure

Adda,

Chiaraviglio,

Franci

et al. 2023

IEEE Trans. Instrum. Meas.

Self Cite

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“…The measurements were repeated 3 times over the journey: in the morning (10-11 a.m.), at noon (12-2 p.m.), and in the afternoon (4-5 p.m.), in order to take into consideration the time-varying traffic of the connected base station. We note that the measurements at night, which have the lowest traffic load (23), were not performed due to the requirement of human intervention. We focus in this work on predicting the UE UL TX power while connected to 4G networks.…”

Section: Measurement Description and Protocolmentioning

confidence: 99%

Artificial Neural Network-Based Uplink Power Prediction From Multi-Floor Indoor Measurement Campaigns in 4G Networks

Mazloum

Wang

Hamdi

et al. 2021

Front. Public Health

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Paving the path toward the fifth generation (5G) of wireless networks with a huge increase in the number of user equipment has strengthened public concerns on human exposure to radio-frequency electromagnetic fields (RF EMFs). This requires an assessment and monitoring of RF EMF exposure, in an almost continuous way. Particular interest goes to the uplink (UL) exposure, assessed through the transmission power of the mobile phone, due to its close proximity to the human body. However, the UL transmit (TX) power is not provided by the off-the-shelf modem and RF devices. In this context, we first conduct measurement campaigns in a multi-floor indoor environment using a drive test solution to record both downlink (DL) and UL connection parameters for Long Term Evolution (LTE) networks. Several usage services (including WhatsApp voice calls, WhatsApp video calls, and file uploading) are investigated in the measurement campaigns. Then, we propose an artificial neural network (ANN) model to estimate the UL TX power, by exploiting easily available parameters such as the DL connection indicators and the information related to an indoor environment. With those easy-accessed input features, the proposed ANN model is able to obtain an accurate estimation of UL TX power with a mean absolute error (MAE) of 1.487 dB.

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“…Therefore, it is essential to evaluate the electromagnetic radiation level of 5G base stations to ensure compliance with applicable limits and to provide optimal site selection for radio wave coverage. Currently, there are mainly four approaches to obtain the electromagnetic radiation level of base stations: measurement near base stations using a field strength analyser [4][5][6][7][8][9], electromagnetic simulation [10][11][12][13], the extrapolation technique [14][15][16] and time series analysis [17,18].…”

Section: Introductionmentioning

confidence: 99%

“…al. built a data set based on 36‐month measured EMF and used standard models and Artificial Neural Network (ANN) models, respectively, to forecast the next 12‐month EMF [17]. Based on data measured over 7 days, Pietro De Lellis et.…”

Section: Introductionmentioning

confidence: 99%

Electromagnetic radiation estimation at the ground plane near fifth‐generation base stations in China by using machine learning method

Shi,

Li,

Cui

et al. 2024

IET Microwaves Antenna & Prop

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A novel method based on machine learning is proposed to estimate the electromagnetic radiation level at the ground plane near fifth‐generation (5G) base stations. The machine learning model was trained using data from various 5G base stations, enabling it to estimate the electric field intensity at any arbitrary radiation point when the base station provides service to different numbers of 5G terminals which are in different service modes. The inputs required for the model include the transmit power of the antenna, the antenna gain, the distance between the 5G base station and 5G terminals, terminal service modes, the number of 5G terminals and the environmental complexity around the 5G base station. Experimental results demonstrate the feasibility and effectiveness of the estimation method, with the mean absolute percentage error of the machine learning model being approximately 5.98%. This level of accuracy showcases the reliability of the approach. Moreover, the proposed method offers low costs when compared with on‐site measurements. The estimated results can be utilised to reduce test costs and provide valuable guidance for optimal site selection, thereby facilitating radio wave coverage or electromagnetic radiation regulation of 5G base stations.

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Time-Series-Based Model and Validation for Prediction of Exposure to Wideband Radio Frequency Electromagnetic Radiation

Cited by 13 publications

References 26 publications

High Noon for Mobile Networks: Short-Time EMF Measurements to Capture Daily Exposure

High Noon for Mobile Networks: Short-Time EMF Measurements to Capture Daily Exposure

Artificial Neural Network-Based Uplink Power Prediction From Multi-Floor Indoor Measurement Campaigns in 4G Networks

Electromagnetic radiation estimation at the ground plane near fifth‐generation base stations in China by using machine learning method

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