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

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 almo...

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Autores principales:	Taghrid Mazloum, Shanshan Wang, Maryem Hamdi, Biruk Ashenafi Mulugeta, Joe Wiart
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	EMF exposure indoor uplink LTE transmit power artificial neural networks Public aspects of medicine RA1-1270
Acceso en línea:	https://doaj.org/article/3ddcc9ef9197468791a98cbe61c72b2f
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spelling	oai:doaj.org-article:3ddcc9ef9197468791a98cbe61c72b2f2021-12-01T16:38:08ZArtificial Neural Network-Based Uplink Power Prediction From Multi-Floor Indoor Measurement Campaigns in 4G Networks2296-256510.3389/fpubh.2021.777798https://doaj.org/article/3ddcc9ef9197468791a98cbe61c72b2f2021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fpubh.2021.777798/fullhttps://doaj.org/toc/2296-2565Paving 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.Taghrid MazloumShanshan WangMaryem HamdiBiruk Ashenafi MulugetaJoe WiartFrontiers Media S.A.articleEMF exposureindooruplinkLTEtransmit powerartificial neural networksPublic aspects of medicineRA1-1270ENFrontiers in Public Health, Vol 9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	EMF exposure indoor uplink LTE transmit power artificial neural networks Public aspects of medicine RA1-1270
spellingShingle	EMF exposure indoor uplink LTE transmit power artificial neural networks Public aspects of medicine RA1-1270 Taghrid Mazloum Shanshan Wang Maryem Hamdi Biruk Ashenafi Mulugeta Joe Wiart Artificial Neural Network-Based Uplink Power Prediction From Multi-Floor Indoor Measurement Campaigns in 4G Networks
description	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.
format	article
author	Taghrid Mazloum Shanshan Wang Maryem Hamdi Biruk Ashenafi Mulugeta Joe Wiart
author_facet	Taghrid Mazloum Shanshan Wang Maryem Hamdi Biruk Ashenafi Mulugeta Joe Wiart
author_sort	Taghrid Mazloum
title	Artificial Neural Network-Based Uplink Power Prediction From Multi-Floor Indoor Measurement Campaigns in 4G Networks
title_short	Artificial Neural Network-Based Uplink Power Prediction From Multi-Floor Indoor Measurement Campaigns in 4G Networks
title_full	Artificial Neural Network-Based Uplink Power Prediction From Multi-Floor Indoor Measurement Campaigns in 4G Networks
title_fullStr	Artificial Neural Network-Based Uplink Power Prediction From Multi-Floor Indoor Measurement Campaigns in 4G Networks
title_full_unstemmed	Artificial Neural Network-Based Uplink Power Prediction From Multi-Floor Indoor Measurement Campaigns in 4G Networks
title_sort	artificial neural network-based uplink power prediction from multi-floor indoor measurement campaigns in 4g networks
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/3ddcc9ef9197468791a98cbe61c72b2f
work_keys_str_mv	AT taghridmazloum artificialneuralnetworkbaseduplinkpowerpredictionfrommultifloorindoormeasurementcampaignsin4gnetworks AT shanshanwang artificialneuralnetworkbaseduplinkpowerpredictionfrommultifloorindoormeasurementcampaignsin4gnetworks AT maryemhamdi artificialneuralnetworkbaseduplinkpowerpredictionfrommultifloorindoormeasurementcampaignsin4gnetworks AT birukashenafimulugeta artificialneuralnetworkbaseduplinkpowerpredictionfrommultifloorindoormeasurementcampaignsin4gnetworks AT joewiart artificialneuralnetworkbaseduplinkpowerpredictionfrommultifloorindoormeasurementcampaignsin4gnetworks
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Artificial Neural Network-Based Uplink Power Prediction From Multi-Floor Indoor Measurement Campaigns in 4G Networks

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