The 5G-FR1 Signals: Beams of the Phased Antennas Array and Time-Recurrence of Emissions with

Abstract

The 5G-FR1 Signals: Beams of the Phased Antennas Array and Time-Recurrence of Emissions with Consequences on Human Exposure Deaconescu DB, Miclaus S. The 5G-FR1 Signals: Beams of the Phased Antennas Array and Time- Recurrence of Emissions with Consequences on Human Exposure. Electronics. 2023; 12(2):297. doi: 10.3390/electronics12020297. Abstract The fifth generation (5G) of mobile communication technology poses lots of questions while introducing significant improvements compared with previous generations. The most sensitive question is related to the safety of human exposure. The aim of present work was to analyze, with a few chosen examples, two of the most significant features of 5G emissions: the extreme spatial variability of the exposure and the nonlinear dynamics characteristics of the temporal variability of the exposure. Two models of patch antenna arrays operating at 3.7 GHz with varying beam forming and beam steering capabilities were considered for an analysis of the specific absorption rate of electromagnetic energy deposition in tissues of a head model. This allowed clear emphasis on the influence of the antenna geometry and feeding peculiarities on the spatial variability of exposure. The second approach implemented the original idea of following the nonlinear recurrence behavior of exposure in time, and underlined the time variability characteristics of emissions with a real-life mobile phone running different 5G applications. Time series of the emitted electric-field strengths were recorded by means a real-time spectrum analyzer and two near-field probes differently positioned in the beam. The presence of laminar emissions, chaotic emissions, determinism and recurrence in the exposures prove the potential for recurrence quantification in predicting time variability features of 5G exposure. Overall, the impact of 5G signals on living bodies, with the highest possible man-made spatial and temporal variability, may have very unpredictable bio-medical consequences. Open access paper: mdpi.com

AI evidence extraction

Main findings

Using two patch antenna array models operating at 3.7 GHz, the study analyzed SAR in a head model and emphasized that antenna geometry and feeding characteristics influence spatial variability of exposure. Measurements with a real-life mobile phone running different 5G applications recorded time series of emitted electric-field strengths; the authors report laminar and chaotic emissions, determinism, and recurrence, suggesting recurrence quantification could help predict time-variability features of 5G exposure.

Outcomes measured

• Specific absorption rate (SAR) in head model tissues
• Emitted electric-field strength time series (near-field measurements)
• Spatial variability of exposure (beamforming/beam steering)
• Temporal variability / nonlinear recurrence characteristics of emissions

Limitations

• Study described as using 'a few chosen examples'
• No quantitative SAR or field-strength results reported in the provided abstract
• No health outcomes measured; focuses on exposure characteristics and modeling/measurement
• Details on phone model, applications, and measurement protocol not provided in the abstract

Suggested hubs

• 5g-policy (0.6)
  Focuses on 5G FR1 exposure characteristics relevant to safety/exposure discussions.

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        "frequency_mhz": 3700,
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    },
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    "outcomes": [
        "Specific absorption rate (SAR) in head model tissues",
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        "Temporal variability / nonlinear recurrence characteristics of emissions"
    ],
    "main_findings": "Using two patch antenna array models operating at 3.7 GHz, the study analyzed SAR in a head model and emphasized that antenna geometry and feeding characteristics influence spatial variability of exposure. Measurements with a real-life mobile phone running different 5G applications recorded time series of emitted electric-field strengths; the authors report laminar and chaotic emissions, determinism, and recurrence, suggesting recurrence quantification could help predict time-variability features of 5G exposure.",
    "effect_direction": "unclear",
    "limitations": [
        "Study described as using 'a few chosen examples'",
        "No quantitative SAR or field-strength results reported in the provided abstract",
        "No health outcomes measured; focuses on exposure characteristics and modeling/measurement",
        "Details on phone model, applications, and measurement protocol not provided in the abstract"
    ],
    "evidence_strength": "insufficient",
    "confidence": 0.7399999999999999911182158029987476766109466552734375,
    "peer_reviewed_likely": "yes",
    "keywords": [
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        "phased antenna array",
        "patch antenna array",
        "SAR",
        "head model",
        "near-field probes",
        "spectrum analyzer",
        "electric-field strength",
        "temporal variability",
        "spatial variability",
        "recurrence quantification",
        "nonlinear dynamics"
    ],
    "suggested_hubs": [
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