The Dynamics of the Radiated Field Near a Mobile Phone Connected to a 4G or 5G Network

2022 · Unclear · Low evidence

← Research stream

UnclearLow evidencePaper 859

Study snapshot

Publicationmanual · 2022

Study typeexposure_assessment

Reported directionUnclear

Evidence ratingLow

Abstract

The Dynamics of the Radiated Field Near a Mobile Phone Connected to a 4G or 5G Network D. B. Deaconescu, A. M. Buda, D. Vatamanu, S. Miclaus. The Dynamics of the Radiated Field Near a Mobile Phone Connected to a 4G or 5G Network. Eng. Technol. Appl. Sci. Res. 12(1):8101–8106, Feb. 2022. Abstract Characterizing the time variations of signals emitted by mobile terminals provides complementary information to health authorities, especially with the increase of frequency and energy of radiation towards millimeter waves. This experimental work aimed to quantify and classify the time variability of the electric field level measured at 10cm from a mobile phone connected sequentially to a 4th and 5th generation mobile network. Statistic analysis was performed on data from real-time spectrum analyzers, while self-similarity was computed by first recurrence plots of the radiated emissions, corresponding to five different types of mobile applications. Moreover, specificities to the communication standard and the type of application were identified. Conclusions This study aimed to identify the differences in the time variability of signals emitted by a mobile phone when sequentially connected to a 4G-LTE or 5G-NR network. The main reason behind this study was the need of describing not only the dose of radiation absorbed by a human body while using a phone but also the average or the momentary dose rate. While the present safety exposure standards do not yet expressly require such knowledge, increasing the frequency towards tens of GHz in 5G networks means an increase in the energy of the radiation so it might need a time-dependent description similar to the ionizing radiation. Moreover, the averaging time used to describe the biological impact of the field is also very important to accurately describe the safety of people exposed to very short and quasi-stochastic pulses [31]. By experimentally determining the E-field strength variation during the 25s usage of 5 different mobile communication activities in either 4G or 5G networks, which allowed TDD technique and respectively had 20 and 40MHz bandwidths for the transmission, streams of 250 field values were gathered and treated as time- series. The boxplot representations of these data allowed obtaining descriptive statistic distributions, while the Poincare plots showed self similarity by first recurrence means. The results showed that: a) more intensive dynamics are encountered generally in 5G UL signal than in 4G, b) the time variability is dependent not only on the communication standard but also on the type of mobile application used, c) short-term variability is lower than long term when using a 4G application, as compared to 5G where the short-term variability of field level is higher than the long term, d) the lowest ratio between short- and long-time variabilities was encountered for file upload in 4G, while the highest ratio was encountered for file download in 5G, and e) the largest total variabilities in 4G were associated to upload and video call, while in 5G they were noted during voice call and file download. These results provide important knowledge that completes the picture of amplitude-time variation of human exposure to near-the-body used mobile devices. Open access paper: etasr.com

Machine Enhanced Logic

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 · 25 s per activity (5 activities)

Evidence strengthLowConfidence: 74% · Peer reviewed: unknown

Main findings

In measurements at 10 cm from a mobile phone sequentially connected to 4G-LTE and 5G-NR, the emitted E-field time variability depended on both the communication standard and the type of mobile application. The authors report generally more intensive dynamics in 5G uplink than in 4G, and differing patterns of short-term versus long-term variability between 4G and 5G across activities.

Outcomes measured

Electric-field (E-field) strength level at 10 cm from a mobile phone
Time variability/dynamics of emitted signals (short-term vs long-term variability)
Self-similarity/recurrence characteristics (Poincaré/recurrence plots)
Differences by network standard (4G-LTE vs 5G-NR) and application type

Limitations

No human participants or health outcomes assessed (exposure characterization only)
Frequency and SAR not reported in the abstract
Sample size/number of phones and measurement repetitions not reported in the abstract
Measurements reported at a single distance (10 cm) and short observation windows (25 s per activity)

View raw extracted JSON

{
  "study_type": "exposure_assessment",
  "exposure": {
    "band": "RF",
    "source": "mobile phone",
    "frequency_mhz": null,
    "sar_wkg": null,
    "duration": "25 s per activity (5 activities)"
  },
  "population": null,
  "sample_size": null,
  "outcomes": [
    "Electric-field (E-field) strength level at 10 cm from a mobile phone",
    "Time variability/dynamics of emitted signals (short-term vs long-term variability)",
    "Self-similarity/recurrence characteristics (Poincaré/recurrence plots)",
    "Differences by network standard (4G-LTE vs 5G-NR) and application type"
  ],
  "main_findings": "In measurements at 10 cm from a mobile phone sequentially connected to 4G-LTE and 5G-NR, the emitted E-field time variability depended on both the communication standard and the type of mobile application. The authors report generally more intensive dynamics in 5G uplink than in 4G, and differing patterns of short-term versus long-term variability between 4G and 5G across activities.",
  "effect_direction": "unclear",
  "limitations": [
    "No human participants or health outcomes assessed (exposure characterization only)",
    "Frequency and SAR not reported in the abstract",
    "Sample size/number of phones and measurement repetitions not reported in the abstract",
    "Measurements reported at a single distance (10 cm) and short observation windows (25 s per activity)"
  ],
  "evidence_strength": "low",
  "confidence": 0.74,
  "peer_reviewed_likely": "unknown",
  "keywords": [
    "4G-LTE",
    "5G-NR",
    "mobile phone",
    "near-field exposure",
    "electric field",
    "time variability",
    "uplink",
    "TDD",
    "bandwidth 20 MHz",
    "bandwidth 40 MHz",
    "spectrum analyzer",
    "recurrence plot",
    "Poincaré plot"
  ],
  "suggested_hubs": [
    {
      "slug": "5g-policy",
      "weight": 0.32,
      "reason": "Study compares 4G vs 5G emissions and discusses implications for exposure characterization/averaging time."
    }
  ]
}

AI can be wrong. Always verify against the paper.

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: Not supplied
PubMed: Not supplied

Read the original paper →

Related papers

← Back to the Research Library