Experimental analysis of 5G pilot signals' variability in urban scenarios

Betta, G.; Capriglione, Domenico; Cerro, Gianni; Miele, Gianfranco; Migliore, Marco Donald; Suka, Darko S.

doi:10.1109/mn55117.2022.9887762

Cited by 7 publications

(4 citation statements)

References 23 publications

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“…Focusing on G1 [11]- [15], Chiaraviglio et al [11] perform a massive evaluation of a 5G RBS covering a town. Betta et al [12], [13] and Elbasheir et al [14] collect RBS exposure information through the measurement of the pilot signals. [22], Heliot et al [23] share the idea of measuring the exposure from 5G RBSs by forcing traffic with a terminal in the DL direction from the RBS.…”

Section: Exposure Measurements From 5g Rbsmentioning

confidence: 99%

Dominance of Smartphone Exposure in 5G Mobile Networks

Chiaraviglio

Lodovisi

Bartoletti

et al. 2024

IEEE Trans. on Mobile Comput.

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The deployment of 5G networks is sometimes questioned due to the impact of ElectroMagnetic Field (EMF) generated by Radio Base Station (RBS) on users. The goal of this work is to analyze such issue from a novel perspective, by comparing RBS EMF against exposure generated by 5G smartphones in commercial deployments. The measurement of exposure from 5G is hampered by several implementation aspects, such as dual connectivity between 4G and 5G, spectrum fragmentation, and carrier aggregation. To face such issues, we deploy a novel framework, called 5G-EA, tailored to the assessment of smartphone and RBS exposure through an innovative measurement algorithm, able to remotely control a programmable spectrum analyzer. Results, obtained in both outdoor and indoor locations, reveal that smartphone exposure (upon generation of uplink traffic) dominates over the RBS one. Moreover, Line-of-Sight locations experience a reduction of around one order of magnitude on the overall exposure compared to Non-Line-of-Sight ones. In addition, 5G exposure always represents a small share (up to 38%) compared to the total one radiated by the smartphone.

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Section: Exposure Measurements From 5g Rbsmentioning

confidence: 99%

Dominance of Smartphone Exposure in 5G Mobile Networks

Chiaraviglio

Lodovisi

Bartoletti

et al. 2024

IEEE Trans. on Mobile Comput.

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“…It should be mentioned that most of the research has been conducted on base station emissions in relation to human EMF exposure to 4G and 5G signals. In this regard, measurement procedures have been proposed and refined [18][19][20][21], tests and monitoring have been settled, scenarios have been analyzed [22][23][24], and much knowledge has been gathered. Opposite, the user device as a source of EMF exposure has been much less investigated, mainly in realistic usage conditions, even if multiple studies have been devoted to dosimetry to compare the specific absorption rate (SAR) of energy deposition in the head/brain to the regulated safety limits [25,26].…”

Section: Introductionmentioning

confidence: 99%

An Exposimetric Electromagnetic Comparison of Mobile Phone Emissions: 5G versus 4G Signals Analyses by Means of Statistics and Convolutional Neural Networks Classification

Miclaus,

Deaconescu,

Vatamanu

et al. 2023

Technologies

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To gain a deeper understanding of the hotly contested topic of the non-thermal biological effects of microwaves, new metrics and methodologies need to be adopted. The direction proposed in the current work, which includes peak exposure analysis and not just time-averaged analysis, aligns well with this objective. The proposed methodology is not intended to facilitate a comparison of the general characteristics between 4G and 5G mobile communication signals. Instead, its purpose is to provide a means for analyzing specific real-life exposure conditions that may vary based on multiple parameters. A differentiation based on amplitude-time features of the 4G versus 5G signals is followed, with the aim of describing the peculiarities of a user’s exposure when he runs four types of mobile applications on his mobile phone on either of the two mobile networks. To achieve the goals, we used signal and spectrum analyzers with adequate real-time analysis bandwidths and statistical descriptions provided by the amplitude probability density (APD) function, the complementary cumulative distribution function (CCDF), channel power measurements, and recorded spectrogram databases. We compared the exposimetric descriptors of emissions specific to file download, file upload, Internet video streaming, and video call usage in both 4G and 5G networks based on the specific modulation and coding schemes. The highest and lowest electric field strengths measured in the air at a 10 cm distance from the phone during emissions are indicated. The power distribution functions with the highest prevalence are highlighted and commented on. Afterwards, the capability of a convolutional neural network that belongs to the family of single-shot detectors is proven to recognize and classify the emissions with a very high degree of accuracy, enabling traceability of the dynamics of human exposure.

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“…The research presented in the article is complementary to other works spanning the 5G measurement world. Betta et al [16] present general issues and research topics related to the measurement of human exposure to cellular networks, while [17], [18], [19], [20], [21], and [22] present the results and discussions more specific to 5G measurements methodologies and the results of experimental campaigns in urban scenarios. Furthermore, some of the authors have recently published the results of an extended experimental campaign about the impact of 5G signals on the response of wideband electric filed probes [23]; while in [24], a methodology to assess human exposure based on scalar and vector measurements of the traffic signals is proposed, following the results of [25].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Methodology to Characterize 3-D Power Monitoring and Control Features for 5G NR Systems Embedding Multi-User MIMO Antennas

Adda

Aureli

D’Elia

et al. 2023

IEEE Trans. Instrum. Meas.

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Multi-User MIMO (MU-MIMO) is widely used in the fifth generation (5G) cellular network to improve network performance at high traffic loads. When demonstrating the compliance of 5G antennas with the radio frequency (RF) electromagnetic fields standards, beamforming and MU-MIMO must be considered, since a 5G MU-MIMO antenna radiation pattern changes in both the space and time domain, adapting to changes of the user's position. To cope with the time and space variability of the power radiated by 5G systems, mobile operators can activate automatic tools to monitor and control the transmitted power (power lock (PL) systems) to ensure that a given radiated power threshold is not exceeded. This article extends and enhances the methodology introduced by the authors in previous work, by testing and verifying PL systems in an MU-MIMO and beamforming scenario (3-D PL systems, 3DPL) through field measurements, controlling the power transmitted by each beam, i.e., in each direction. Measurement results confirm that the 3DPL feature limits the average power of the traffic channels transmitted in each direction, regardless of the configured total maximum power. More specifically, it is shown that the 3DPL reduces the maximum average transmitted power by the expected 2 dB when only one user is active (i.e., in a singleuser MIMO (SU-MIMO) scenario), while it does not operate any reduction when two users are active (MU-MIMO scenario) because the power transmitted toward each user is already half (i.e., −3 dB) of the maximum power that can be transmitted.

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Experimental analysis of 5G pilot signals' variability in urban scenarios

Cited by 7 publications

References 23 publications

Dominance of Smartphone Exposure in 5G Mobile Networks

Dominance of Smartphone Exposure in 5G Mobile Networks

An Exposimetric Electromagnetic Comparison of Mobile Phone Emissions: 5G versus 4G Signals Analyses by Means of Statistics and Convolutional Neural Networks Classification

Enhanced Methodology to Characterize 3-D Power Monitoring and Control Features for 5G NR Systems Embedding Multi-User MIMO Antennas

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