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電波ばく露レベルモニタリング

28GHz 帯 5G システムの電波ばく露レベルモニタリングと深層学習技術のモニタリング分野への応用

EMF Exposure Monitoring of 28-GHz-Band 5G Systems and Applications of Deep
Learning Techniques in the Field of EMF Exposure Monitoring
劉森

大西 輝夫

多氣 昌生

渡辺 聡一

LIU Sen, ONISHI Teruo, TAKI Masao, and WATANABE Soichi

第 5 世代（5G）携帯電話システムでは、従来の携帯電話では使用されていなかった高い周波数帯の 28 GHz 帯も使用している。したがって、実環境で 28 GHz 帯の電波ばく露レベルを把握することは重要である。そこで、電波ばく露レベルモニタリングプロジェクトの一環として 28 GHz
帯の電波ばく露レベル評価の検討を行った。本稿では、まず 28 GHz 帯のローカル 5G 基地局の電波ばく露測定について報告する。一方、測定には多大なリソースが必要であり、深層学習、特に人工ニューラルネットワーク（ANN）を用いることにより、電波ばく露レベルを短時間で推定可能となることが期待できる。筆者は、電波ばく露レベル分布を正確かつ効率的に予測するためのANN モデルを提案し有効性の検証を行ったので報告する。

The 5th-generation (5G) mobile phone system uses the 28 GHz band frequency spectrum thathas never before been used in conventional cellular phone systems. Therefore, it is important tounderstand the electromagnetic field (EMF) exposure level in the 28 GHz band resulting from thesystem in real-world environments. As part of the research project of Acquisition, Accumulation, andApplications of EMF Exposure Monitoring Data, the EMF exposure level in 28 GHz band wasevaluated. This paper first discusses the EMF exposure measurement of 28-GHz-band local 5G(L5G) base stations. On the other hand, significant resources are required for these measurements,
and by using deep learning techniques, particularly artificial neural networks (ANNs), EMF exposurelevels are expected to be estimated efficiently. The author thus has proposed an ANN model for
predicting the EMF exposure level distribution accurately and efficiently and reports on its effectiveness.

1

Introduction

Grasping the electromagnetic field (EMF) exposurelevel in real living scenarios is a key step towards ensuring
environmental and occupational health. Financially supported by the Ministry of Internal Affairs andCommunications (MIC), as part of our monitoring project,
we have conducted a series of measurements [1][2] for that
purpose. Currently, fifth-generation (5G) wireless communication is paving its way under the promise of providing society with unprecedented services [3] including
enhanced mobile broadband (eMBB), ultrareliable low-latency communication (URLLC), and massive machine-typecommunication (mMTC). To that end, 5G, for the firsttime in the history of cellular communications, is deployedat two frequency bands including frequency range 1 (FR1:
sub-6GHz) and FR2 (sub-millimeter-wave or millimeterwave band) and employs advanced techniques in thephysical layer including beamforming and massive multiple
input multiple output (mMIMO). Beamforming [4] refers
to generating a radiating beam that points to a target user
at a specific location with respect to the base station (BS)in order to largely mitigate the interference between users
and overcome severe radio-wave propagation path loss.
mMIMO [5] denotes large-scale MIMO techniques tosignificantly improve the capacity and throughput of thesystem. All these certainly strengthen the necessity ofgrasping the EMF exposure level from 5G systems, especially 5G FR2 systems that employ sub-millimeter-waves or
millimeter-waves. Therefore, many on-site measurements
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near 28-GHz-band 5G FR2 BSs have been conducted byresearch groups from all over the world [6]–[11]. Our
measurement results are summarized in Section 2. On theother hand, the many measurements that have been conducted motivate the research society to explore possible
approaches to efficiently predict the EMF exposure level toovercome the heavy resource consumption of performing
on-site measurements. The EMF exposure level is fully
dependent on the environment itself, including the geometry, material, and electromagnetic (EM) source. Withadvanced numerical approaches (e.g., ray tracing [12], finite
difference time domain (FDTD) [13], etc.), one can accurately obtain the EMF exposure level of the scenario under
test. However, it is still inefficient, as the computationaltime could be hours for a large-scale scenario. As an important branch of machine learning and deep learning,
artificial neural networks (ANNs) have attracted considerable attention in recent years and have achieved tremendous
success in many domains such as physics, biochemistry,and social sciences [14]–[15]. With an appropriate networkarchitecture, an ANN model can not only accurately capture the general pattern hiding within the training data but
also be generalized to new data that have never been “seen”.

Table 1

BS information (non-standalone)

Frequency

28.2 – 28.3 GHz(bandwidth: 100 MHz)

Subcarrier spacing

120 kHz

Number of synchronization beams

64

Table 2The most appealing feature is that the model can efficiently yield prediction results once it is trained, usuallymuch faster than conventional numerical approaches.
Therefore, with the final goal of generalizing to 28-GHzband 5G systems in mind, the author has preliminarily
developed an ANN model for predicting the EMF exposurelevel resulting from a dipole antenna operating at 28 GHz.
This is discussed in Section 3. EMF exposure monitoringand machine learning will be inseparable in the future,
giving rise to a new branch of research. The future prospect
is discussed in Section 4.

2
2.1

EMF Exposure Monitoring of 28-GHzBand 5G Systems [16]
Monitoring approachOn-site measurements were conducted in the vicinityof a 28-GHz-band local 5G (L5G) BS deployed on thecampus of Tokyo Metropolitan University in Tokyo, Japan.
Here, L5G [17] is a 5G system unique to Japan and isspecially designed for a local area such as a university ora factory. The information of the BS is summarized inTable 1. Figure 1 shows the measurement site. The BS is
deployed on the roof of a research building at a height ofaround 25 meters from the ground and is facing the open
area in front of the building. A receiving antenna is placed
at a distance of around 35 meters away from the BS and isconnected to a portable spectrum analyzer (Anritsu
MS2090A) to measure the strength of the electric field(E-field) resulting from the BS. The antenna height is 1.5

Configurations of measurement sets

Measurement Set

Antenna

Set-1
Set-2
Set-3

Horn1 (gain: 14.4 dBi)
Omni2 (gain: 1.7 dBi)
Omni2 (gain: 1.7 dBi)

Smartphone position and distance both
relative to the antenna
Back (0.5 m)
Back (0.5 m)
Back (2.0 m)

Horn: DRH50 (RFspin)
Omni: 01S1165-00 (Waka Manufacturing)

1
2

Table 3

Traffic condition for every sub-measurement

Name
Power off
Power onTraffic condition
The smartphone is powered off
The smartphone is powered on but without data traffic (no traffic)

Data rate [Mbps]-

iPerf3-DL

The smartphone is continuously running the iPerf3* service in thedownload direction

500 (max)

iPerf3-UL

The smartphone is continuously running the iPerf3* service in the
upload direction

20–40*iPerf3: a tool for generating synthetic traffic between the smartphone and the BS.

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meters from the ground. A 5G smartphone is placed in thevicinity of the receiving antenna to ensure the radiating
beam of the BS points in the direction of the receiving
antenna. There are three sets of measurements in different
configurations, as listed in Table 2. The smartphone isplaced behind the antenna for all the measurement sets.
The smartphone, once powered on, will always have sometransmitted power and thus could affect the measurement
results. On the one hand, as the horn antenna has a directional radiation pattern, such an effect is easily blocked. On
the other hand, for measurement sets using the omni antenna (Sets 2 and 3), different distances between thesmartphone and the antenna are adopted to investigate thiseffect. In addition, there are four sub-measurements with

Table 4

Spectrum analyzer (Anritsu MS2090A) settings

Frequency range

28.2 – 28.3 GHz

Resolution bandwidth (RBW)

1 MHz

Video bandwidth (VBW)

0.01 MHz

Points101

Sweep time

≈ 6 msFig. 1

Measurement site(a)Fig. 2each one having a different data traffic condition for eachmeasurement set. These data traffic conditions are listed inTable 3. For every sub-measurement, the measurement
time is 1 min. The settings of the spectrum analyzer are
summarized in Table 4.

2.2

Monitoring results

Figures 2 (a) and (b) depict the root mean square(RMS) and maximum (both over 1 min) E-field strengths
for every traffic condition in every measurement set, respectively. For each measurement set, the maximum values
show little difference between Power on, iPerf3-DL, andiPerf3-UL cases. The RMS E-field strength differences between the iPerf3-DL case and the Power on case are about
24 dB, 19 dB, and 24 dB for Set-1, Set-2, and Set-3, respectively. For all measurement sets, the RMS E-field strengths
follow the same trend of iPerf3-DL > iPerf3-UL > Poweron > Power off.
Figures 3 (a), (b), and (c) depict the cumulative distribution functions (CDFs) of the instantaneous E-fieldvalues for every measurement set, respectively. For all theiPerf3-DL cases shown in Fig. 3, the E-field strengths that
correspond to the rapid increases (blue dashed lines) arealmost the same, indicating that the E-field strengths
purely resulting from the BS are the same. However, there
is a gradient-changing region, as marked in Fig. 3 (b). Thisis caused by the smartphone itself. It is evident that the BS
is not continuously transmitting power during the entire
measurement duration of iPerf3-UL cases since a significant
part of the E-field strength data for this traffic condition isless than the noise threshold marked in Fig. 3 (≈ 60
dBμV/m for Set-1 and 70 dBμV/m for Set-2 and Set-3).
Note that the noise thresholds are different since the antennas have different gains.

(b)(a) RMS E-field and (b) maximum E-field strength results over 1 minute

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Fig. 3

CDFs for (a) Set-1, (b) Set-2, and (c) Set-3

Fig. 4

3

Applications of Deep Learning
Techniques in the Field of EMF
Exposure Monitoring [18]–[19]

3.1

Architecture and model

Prediction model at the top level

The existing monitoring approaches are usually basedon on-site measurements and thus suffer from heavy resource consumption. Aiming at solving this problem, theauthor has developed a machine learning model for accurately and efficiently predicting the EMF exposure level.
The purpose of a machine learning model is to capture theunderlying general pattern, in this case, the physics of radio194

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wave propagations, through learning from the training
data. To this end, the input needs to be represented in an
appropriate manner so that all the features hidden inside
the training data are covered. EMF exposure levels are
fully dependent on the environment and the EM source.
The authors innovatively represent the environment together with the EM source as a graph, and creatively employs graph neural networks [15][20]. A graph is composedof nodes and edges that connect between nodes and canbe well organized to include all the features including thegeometry and materials of the environment and the information of the EM source. The overall prediction model is5-3

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Fig. 5

Fig. 6

Some floorplans in the dataset

Scattering plots for the (a) training subset and (b) validation subset

shown in Fig. 4. A given 3-D geometry of the environment
including the transmitter (Tx) is first modeled to its graphrepresentation in Stage 1 (Graph Modeling). This graphrepresentation is then used as the input to a graph neuralnetwork (GNN) (Stage 2), and is encoded as a vector
representation with all the information (environment andEM source) being implicitly included. Finally, the E-field
distribution is generated using a predictor network (Stage3) with the vector representation as input. Here, the predictor is a fully connected neural network.

3.2

Results

Focusing on indoor environments, the author randomly designed one hundred different indoor floorplans(some of the floorplans are shown in Fig. 5) and split theminto a training subset (90 floorplans) and a validation
subset (10 floorplans). Here, the training subset was used
to guide model training, whereas the validation subset wasused to test the performance of the trained model. For
every floorplan, a dipole antenna operating at 28 GHz was
sequentially placed at 180 positions uniformly distributed

across that floorplan. Wireless InSite (REMCOM Inc.,
USA) software was used to calculate the RMS E-fieldstrength distribution, and these data were used as groundtruths. The evaluation metric used for quantifying theperformance of the prediction model was the mean absolute error (MAE) in dB. The formula is�MAE �dB� �∙ ∑���� ∑����|20 ∙ log 𝑝𝑝�� � 20 ∙ log 𝑟𝑟�� |,�∙�(1)where i is the index of floorplans in either the training
subset or the validation subset, k is the index of observation
points within a floorplan, pik is the predicted value, and rik
is the true value. The scattering plots of the training andvalidation subsets are shown in Figs. 6 (a) and (b), respectively. MAEs of 3.2 dB and 4.2 dB are achieved for the
training subset and validation subset, respectively. These
results are quite good compared with the results of theconventional empirical propagation models [21] that had
an error of more than 10 dB. As shown in Fig. 6 (b), theprediction is uniformly distributed along the ideal line,
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Fig. 7Ground truth and result of prediction

tion in the floorplans of the validation subset. In otherwords, the model can “see” new floorplans that were neverused during the model training process. Figures 7 (a) and(b) respectively depict the true and predicted E-field distributions in a selected data sample from the validation
subset for comparison purposes. It is evident that theprediction model provides excellent results in regions withhigh strength values including line-of-sight regions andnon-line-of-sight regions that are close to the Tx antenna.
Meanwhile, the prediction model provides the results
within 1 s, which is much faster than simulations that require 1.5 min with the option of GPU acceleration enabled.

4

Conclusion and Future Prospects

In this research, the author measured the EMF exposurelevel from a 28-GHz-band local 5G BS deployed on thecampus of Tokyo Metropolitan University in Tokyo, Japan.
It was found that the maximum E-field strength is notdependent on the traffic condition when the smartphone
is powered on. Meanwhile, there is an increase of around
20 dB of the RMS E-field strength in cases with data traffic compared with cases of no traffic. 5G is paving its way,
and many commercial BSs, including underground ones,
will be newly deployed. The authors are currently investigating commercial 5G BSs. The author also developed an
EMF exposure prediction model based on graph neuralnetworks. The model can accurately and efficiently predict
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the EMF exposure level distribution in an indoor environment that is beyond the training subset. The author will
explore the possibility of extending the model to outdoor
scenarios in the future. In addition, the combining of
measured data is also a promising direction for future
exploration.

Acknowledgment
The author appreciates the support from the Ministryof Internal Affairs and Communications, Japan (JPMI10001)
and the Tokyo Metropolitan University (TMU) local 5G
research funding. The author thanks the support from Mr.
Tsuchiya Naoto and Prof. Yukihisa Suzuki from TMU, andMr. Kazuhiro Tobita from our research group.

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大西 輝夫 （おおにし てるお）
電磁波研究所電磁波標準研究センター
電磁環境研究室主任研究員
博士（工学）
環境電磁工学【受賞歴】
2017 年 IEC 1906 賞
2010 年 経済産業省産業技術環境局長表彰
2010 年 日本 ITU 協会賞

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多氣 昌生 （たき まさお）
電磁波研究所電磁波標準研究センター
電磁環境研究室
上席研究員／
東京都立大学名誉教授
システムデザイン研究科
特任教授
工学博士
環境電磁工学【受賞歴】
2020 年 第 70 回「電波の日」 総務大臣表彰
2018 年 電気学会 業績賞
2010 年 平成 22 年度工業標準化事業表彰
経済産業大臣賞

Mag., vol.34, no.4, pp.18-42, July 2017.
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渡辺 聡一 （わたなべ そういち）
電磁波研究所電磁波標準研究センター
電磁環境研究室室長
博士（工学）
環境電磁工学【受賞歴】
2022 年 電波功績賞電波産業会会長表彰
2021 年 電子情報通信学会業績賞
2012 年 平成 24 年度文部科学大臣表彰
科学技術賞

Jan. 2009.
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pp.943–967, July 1993.

Liu Sen （りゅう しん）
電磁波研究所電磁波標準研究センター
電磁環境研究室研究員
博士（工学）
環境電磁工学【受賞歴】
2021 年 アンテナ・伝播研究専門委員会
若手奨励賞
2021 年 Young Scientist Award(URSI GASS 2021)
2021 年 IEEE AP-S Japan Young Engineer
Award

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