The temporal imprint of mobile phone emission level when running various applications in 4G versus 5G networks

2022 · Unclear · Insufficient evidence

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UnclearInsufficient evidencePaper 595

Study snapshot

Publicationmanual · 2022

Study typeexposure_assessment

Reported directionUnclear

Evidence ratingInsufficient

Abstract

The temporal imprint of mobile phone emission level when running various applications in 4G versus 5G networks Miclaus S, Deaconescu DB, Vatamanu D, Buda AM. The temporal imprint of mobile phone emission level when running various applications in 4G versus 5G networks. 2022 International Symposium on Electronics and Telecommunications (ISETC), Timisoara, Romania, 2022, pp. 1-4, doi: 10.1109/ISETC56213.2022.10010000. Abstract Human exposure to electromagnetic fields emitted by mobile communications sources is dominated by the mobile phone radiation. Not only the dose of radiation is important, but also dose- or fluence-rate. In this regard, present research emphasizes the features of the dynamics of mobile terminal emission levels by means of statistics, nonlinear data analysis with recurrence quantification and time-frequency analyses. Time series of radiated electric field strengths were recorded and processed for a set of five mobile applications usage per communication standard. Both 4G-LTE emission channel of 20 MHz bandwidth and 5G-NR FR1 emission channel with a bandwidth of 40 MHz were studied. Differences between 4G and 5G emissions variabilities are presented, and application-specific features of the time imprint are highlighted. Conclusions An original approach was applied here for extracting the temporal imprint of mobile phone emissions when it was successively connected to 4G and 5G (sub-6GHz) networks. The same five types of mobile applications were investigated while running, from the point of view of statistics, recurrence quantification and time-frequency plots of normalized E-field strengths emitted during 24s of use. The most significant findings were: a) upload and video call showed the highest differences between 4G and 5G field level distributions; b) 5G emissions are highly concentrated in the lower levels end, with small differences between applications, which is very dissimilar from 4G emissions; c) video call in 4G conducted to the highest slope of field level local accumulation in time; the same was true in 5G, if compared with the other applications running in the same standard; d) on average the 5G emissions showed coefficient of variations of field levels 45% higher than 4G emissions; e) correlation between 4G and 5G strings of field levels was the largest for file upload but lack of correlation resulted for voice call; f) recurrence quantifications showed a degree of determinism present but no predictability; clear differences between 4G and 5G are emphasized while Poincare plots indicated that short-term variability is always consistently smaller than long-term variability in all 4G applications, and vice-versa is true for 5G emissions; h) time-frequency analysis emphasized again net differences between 4G and 5G emissions mainly for cases of upload, video call and streaming. Both random and chaotic behaviour of emissions was emphasized so we conclude that too short time-series length might have hidden some significant features in the present preliminary results. Future work will be based on longer time- series data processing and analysis. ieeexplore-ieee- org.libproxy.berkeley.edu/stamp/stamp.jsp?tp=&arnumber=10010000&isnumber=10009039

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AI evidence extraction

Structured fields below were generated from the paper’s abstract and metadata to make the evidence easier to inspect. Automated extraction may be incomplete or incorrect; verify important details against the original paper.

Study typeexposure_assessment

Effect directionunclear

PopulationNot supplied

Sample sizeNot supplied

ExposureRF · 24 s per application (time series of normalized E-field strengths)

Evidence strengthInsufficientConfidence: 74% · Peer reviewed: unknown

Main findings

Time series of radiated electric field strengths were recorded for five mobile applications under 4G-LTE (20 MHz) and 5G-NR FR1 (40 MHz). The authors report application-specific differences between 4G and 5G emission variability, including that upload and video call showed the highest differences in field level distributions, and that 5G emissions were more concentrated at lower levels with smaller differences between applications than 4G. They also report higher average coefficients of variation for 5G field levels (45% higher than 4G) and differing correlation patterns between 4G and 5G field-level strings by application (largest for upload; lack of correlation for voice call).

Outcomes measured

Radiated electric field strength time series (mobile terminal emission levels)
Temporal variability/dynamics of emission levels (statistics, recurrence quantification, time-frequency analysis)
Differences in emission variability between 4G-LTE (20 MHz bandwidth) and 5G-NR FR1 sub-6 GHz (40 MHz bandwidth) across applications

Limitations

Preliminary results based on short time-series length (24 s), which the authors state may have hidden significant features
No explicit reporting of number of devices/measurements or sampling scheme in the provided abstract
Frequency (within sub-6 GHz FR1) not specified in the provided abstract
Outcomes are emission dynamics/variability rather than direct health outcomes

View raw extracted JSON

{
  "study_type": "exposure_assessment",
  "exposure": {
    "band": "RF",
    "source": "mobile phone",
    "frequency_mhz": null,
    "sar_wkg": null,
    "duration": "24 s per application (time series of normalized E-field strengths)"
  },
  "population": null,
  "sample_size": null,
  "outcomes": [
    "Radiated electric field strength time series (mobile terminal emission levels)",
    "Temporal variability/dynamics of emission levels (statistics, recurrence quantification, time-frequency analysis)",
    "Differences in emission variability between 4G-LTE (20 MHz bandwidth) and 5G-NR FR1 sub-6 GHz (40 MHz bandwidth) across applications"
  ],
  "main_findings": "Time series of radiated electric field strengths were recorded for five mobile applications under 4G-LTE (20 MHz) and 5G-NR FR1 (40 MHz). The authors report application-specific differences between 4G and 5G emission variability, including that upload and video call showed the highest differences in field level distributions, and that 5G emissions were more concentrated at lower levels with smaller differences between applications than 4G. They also report higher average coefficients of variation for 5G field levels (45% higher than 4G) and differing correlation patterns between 4G and 5G field-level strings by application (largest for upload; lack of correlation for voice call).",
  "effect_direction": "unclear",
  "limitations": [
    "Preliminary results based on short time-series length (24 s), which the authors state may have hidden significant features",
    "No explicit reporting of number of devices/measurements or sampling scheme in the provided abstract",
    "Frequency (within sub-6 GHz FR1) not specified in the provided abstract",
    "Outcomes are emission dynamics/variability rather than direct health outcomes"
  ],
  "evidence_strength": "insufficient",
  "confidence": 0.74,
  "peer_reviewed_likely": "unknown",
  "keywords": [
    "mobile phone",
    "4G-LTE",
    "5G-NR",
    "FR1",
    "sub-6 GHz",
    "electric field strength",
    "emission level",
    "dose-rate",
    "fluence-rate",
    "recurrence quantification",
    "time-frequency analysis",
    "applications",
    "upload",
    "video call",
    "streaming",
    "voice call"
  ],
  "suggested_hubs": [
    {
      "slug": "5g-policy",
      "weight": 0.28,
      "reason": "Compares 4G vs 5G-NR FR1 mobile phone emission characteristics (exposure assessment relevant to 5G discussions)."
    }
  ]
}

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AI-extracted fields are generated from the abstract and metadata and may be incomplete or incorrect. This material is for informational purposes and is not medical advice.

Identifiers and source

DOI: 10.1109/isetc56213.2022.10010000
PubMed: Not supplied

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