toltxlabel=true, tozreflabel=false AReferences

5G-NR

    5G New Radio
3GPP

    3rd Generation Partnership Project
AC

    address coding
ACF

    autocorrelation function
ACR

    autocorrelation receiver
ADC

    Analog-to-Digital Converter
AIC

    Analog-to-Information Converter
AIC

    Akaike information criterion
ARIC

    asymmetric restricted isometry constant
ARIP

    asymmetric restricted isometry property
ARQ

    automatic repeat request
AUB

    asymptotic union bound
AWGN

    Additive White Gaussian Noise
AWGN

    additive white Gaussian noise
PSK

    asymmetric PSK
AWRICs

    asymmetric weak restricted isometry constants
AWRIP

    asymmetric weak restricted isometry property
BCH

    Bose, Chaudhuri, and Hocquenghem
BCHSC

    BCH based source coding
BEP

    bit error probability
BFC

    block fading channel
BG

    Bernoulli-Gaussian
BGG

    Bernoulli-Generalized Gaussian
BPAM

    binary pulse amplitude modulation
BPDN

    Basis Pursuit Denoising
BPPM

    binary pulse position modulation
BPSK

    binary phase shift keying
BPZF

    bandpass zonal filter
BSC

    binary symmetric channels
BU

    Bernoulli-uniform
BER

    bit error rate
BS

    base station
CP

    Cyclic Prefix
CDF

    cumulative distribution function
CDF

    cumulative distribution function
CDF

    cumulative distribution function
CCDF

    complementary cumulative distribution function
CCDF

    complementary CDF
CCDF

    complementary cumulative distribution function
CD

    cooperative diversity
CDMA

    Code Division Multiple Access
ch.f.

    characteristic function
CIR

    channel impulse response
CoSaMP

    compressive sampling matching pursuit
CR

    cognitive radio
CS

    compressed sensing
CS

    Compressed sensing
CS

    compressed sensing
CSI

    channel state information
CCSDS

    consultative committee for space data systems
CC

    convolutional coding
COVID-19

    Coronavirus disease
CAPEX

    CAPital EXpenditures
DAA

    detect and avoid
DAB

    digital audio broadcasting
DCT

    discrete cosine transform
DFT

    discrete Fourier transform
DR

    distortion-rate
DS

    direct sequence
DS-SS

    direct-sequence spread-spectrum
DTR

    differential transmitted-reference
DVB-H

    digital video broadcasting – handheld
DVB-T

    digital video broadcasting – terrestrial
DL

    downlink
DSSS

    Direct Sequence Spread Spectrum
DFT-s-OFDM

    Discrete Fourier Transform-spread-Orthogonal Frequency Division Multiplexing
DAS

    distributed antenna system
DNA

    Deoxyribonucleic Acid
EC

    European Commission
EED

    exact eigenvalues distribution
EIRP

    Equivalent Isotropically Radiated Power
ELP

    equivalent low-pass
eMBB

    Enhanced Mobile Broadband
EMF

    Electro-Magnetic Field
EU

    European union
ELP

    Exposure Limit-based Power
FC

    fusion center
FCC

    Federal Communications Commission
FEC

    forward error correction
FFT

    fast Fourier transform
FH

    frequency-hopping
FH-SS

    frequency-hopping spread-spectrum
FS

    Frame synchronization
FS

    frame synchronization
FDMA

    Frequency Division Multiple Access
FSPL

    Free Space Path Loss
FWA

    Fixed Wireless Access
FDD

    Frequency Division Duplexing
GA

    Gaussian approximation
GF

    Galois field
GG

    Generalized-Gaussian
GIC

    generalized information criterion
GLRT

    generalized likelihood ratio test
GPS

    Global Positioning System
GMSK

    Gaussian minimum shift keying
GSMA

    Global System for Mobile communications Association
HAP

    high altitude platform
HW

    HardWare
IDR

    information distortion-rate
IFFT

    inverse fast Fourier transform
IHT

    iterative hard thresholding
i.i.d.

    independent, identically distributed
IoT

    Internet of Things
IR

    impulse radio
LRIC

    lower restricted isometry constant
LRICt

    lower restricted isometry constant threshold
ISI

    intersymbol interference
ITU

    International Telecommunication Union
ICNIRP

    International Commission on Non-Ionizing Radiation Protection
IEEE

    Institute of Electrical and Electronics Engineers
ICES

    IEEE international committee on electromagnetic safety
IEC

    International Electrotechnical Commission
IARC

    International Agency on Research on Cancer
IS-95

    Interim Standard 95
ITU

    International Telecommunication Union
IP

    Internet Protocol
LEO

    low earth orbit
LF

    likelihood function
LLF

    log-likelihood function
LLR

    log-likelihood ratio
LLRT

    log-likelihood ratio test
LOS

    Line-of-Sight
LRT

    likelihood ratio test
LWRIC

    lower weak restricted isometry constant
LWRICt

    LWRIC threshold
LPWAN

    low power wide area network
LoRaWAN

    Low power long Range Wide Area Network
LB

    Lower Bound
MB

    multiband
MC

    multicarrier
MDS

    mixed distributed source
MF

    matched filter
m.g.f.

    moment generating function
MI

    mutual information
MIMO

    multiple-input multiple-output
MISO

    multiple-input single-output
MJSO

    maximum joint support cardinality
ML

    maximum likelihood
MMSE

    minimum mean-square error
MMV

    multiple measurement vectors
MOS

    model order selection
${M}$-PSK

     $M$-ary phase shift keying
${M}$-PSK

     $M$-ary asymmetric PSK
MSP

    Minimum Sensitivity-based Power
$M$-QAM

     $M$-ary quadrature amplitude modulation
MRC

    maximal ratio combiner
MSO

    maximum sparsity order
M2M

    machine to machine
MUI

    multi-user interference
mMTC

    massive Machine Type Communications
mm-Wave

    millimeter-wave
MP

    mobile phone
MPE

    maximum permissible exposure
MAC

    media access control
NB

    narrowband
NBI

    narrowband interference
NLA

    nonlinear sparse approximation
NLOS

    Non-Line-of-Sight
NTIA

    National Telecommunications and Information Administration
NTP

    National Toxicology Program
NHS

    National Health Service
NSA

    Non Stand Alone
OC

    optimum combining
OC

    optimum combining
ODE

    operational distortion-energy
ODR

    operational distortion-rate
OFDM

    orthogonal frequency-division multiplexing
OMP

    orthogonal matching pursuit
OSMP

    orthogonal subspace matching pursuit
OQAM

    offset quadrature amplitude modulation
OQPSK

    offset QPSK
OFDMA

    Orthogonal Frequency-division Multiple Access
OPEX

    OPerating EXpenditures
OQPSK/PM

    OQPSK with phase modulation
PAM

    pulse amplitude modulation
PAR

    peak-to-average ratio
PDF

    probability density function
PDF

    probability density function
PDF

    probability distribution function
PDP

    power dispersion profile
PMF

    probability mass function
PMF

    probability mass function
PN

    pseudo-noise
PPM

    pulse position modulation
PRake

    Partial Rake
PSD

    power spectral density
PSEP

    pairwise synchronization error probability
PSK

    phase shift keying
PD

    Power Density
$8$-PSK

     $8$-phase shift keying
FSK

    frequency shift keying
QAM

    Quadrature Amplitude Modulation
QPSK

    quadrature phase shift keying
OQPSK/PM

    OQPSK with phase modulator
RD

    raw data
RDL

    ”random data limit”
RIC

    restricted isometry constant
RICt

    restricted isometry constant threshold
RIP

    restricted isometry property
ROC

    receiver operating characteristic
RQ

    Raleigh quotient
RS

    Reed-Solomon
RSSC

    RS based source coding
RFP

    Radio Frequency “Pollution”
r.v.

    random variable
R.V.

    random vector
RMS

    root mean square
RFR

    radiofrequency radiation
RIS

    Reconfigurable Intelligent Surface
RNA

    RiboNucleic Acid
RBS

    Radio Base Station
RSRP

    Reference Signal Received Power
RSRQ

    Reference Signal Received Quality
SA

    Stand Alone
SAN

    Spectrum ANalyzer
SCBSES

    Source Compression Based Syndrome Encoding Scheme
SCM

    sample covariance matrix
SEP

    symbol error probability
SG

    sparse-land Gaussian model
SIMO

    single-input multiple-output
SINR

    Signal-to-Interference plus Noise Ratio
SIR

    signal-to-interference ratio
SISO

    single-input single-output
SMV

    single measurement vector
SNR

    signal-to-noise ratio
SP

    subspace pursuit
SS

    spread spectrum
SW

    sync word
SAR

    Specific Absorption Rate
SSB

    synchronization signal block
SCPI

    Standard Commands for Programmable Instruments
SS-RSRP

    Synchronization Signal Reference Signal Received Power
TH

    time-hopping
ToA

    time-of-arrival
TR

    transmitted-reference
TW

    Tracy-Widom
TWDT

    TW Distribution Tail
TCM

    trellis coded modulation
TDD

    Time Division Duplexing
TDMA

    Time Division Multiple Access
TCP

    Transmission Control Protocol
UAV

    unmanned aerial vehicle
URIC

    upper restricted isometry constant
URICt

    upper restricted isometry constant threshold
UWB

    ultrawide band
UWB

    Ultrawide band
URLLC

    Ultra Reliable Low Latency Communications
UWRIC

    upper weak restricted isometry constant
UWRICt

    UWRIC threshold
UE

    User Equipment
UL

    uplink
UB

    Upper Bound
WiM

    weigh-in-motion
WLAN

    wireless local area network
WM

    Wishart matrix
WMAN

    wireless metropolitan area network
WPAN

    wireless personal area network
WRIC

    weak restricted isometry constant
WRICt

    weak restricted isometry constant thresholds
WRIP

    weak restricted isometry property
WSN

    wireless sensor network
WSS

    wide-sense stationary
WHO

    World Health Organization
Wi-Fi

    wireless fidelity
SpaSoSEnc

    sparse source syndrome encoding
VLC

    visible light communication
VPN

    virtual private network
RF

    Radio-Frequency
FSO

    free space optics
IoST

    Internet of space things
GSM

    Global System for Mobile Communications
2G

    second-generation cellular network
3G

    third-generation cellular network
4G

    fourth-generation cellular network
5G

    5th-generation cellular network
gNB

    next-generation Node-B
NR

    New Radio
UMTS

    Universal Mobile Telecommunications Service
LTE

    Long Term Evolution
QoS

    Quality of Service

# Dominance of Smartphone Exposure
in 5G Mobile Networks

Luca Chiaraviglio,(1,2) Chiara Lodovisi,(1,2) Stefania Bartoletti,(1,2) Ahmed
Elzanaty,(3) Mohamed Slim-Alouini,(4)
(1) EE Department, University of Rome Tor Vergata, Rome, Italy, email
<EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS>
(2) Consorzio Nazionale Interuniversitario per le Telecomunicazioni, Italy
(3) Institute for Communication Systems (ICS), University of Surrey,
Guildford, United Kingdom, email<EMAIL_ADDRESS>
(4) CEMSE Division, King Abdullah University of Science and Technology,
Thuwal, Saudi Arabia, email<EMAIL_ADDRESS>
###### Abstract

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.

##  1 Introduction

According to recent reports [1], more than 80% of the world population own a
smartphone. The diffusion of such equipment is so pervasive in the daily
activities that it is almost impossible to imagine a future without a
smartphone in our hands. One of the key drivers for the ever-increasing
smartphone adoption is the ubiquitous Internet service, generally offered by
mobile networks. To this purpose, 5G aims at delivering a true broadband
connectivity service, especially in urban areas and densely populated zones.
The sales of smartphone equipped with 5G interfaces are constantly rising,
with more than 700 millions of units sold during 2022 [2], in parallel with
the deployment of 5G networks across the world [3]. Therefore, 5G networks
will (likely) become the main provider for smartphone connectivity in the near
future.

In this scenario, the Electro-Magnetic Field (EMF) exposure from 5G networks
is a hot topic in several communities (e.g., government, local committees,
environmental protection agencies and academia), especially when considering
the (possible, yet still not proven) implications of 5G exposure on the human
health [4]. To this aim, EMF working groups of World Health Organization (WHO)
[5], International Commission on Non-Ionizing Radiation Protection (ICNIRP)
[6], Institute of Electrical and Electronics Engineers (IEEE) committees [7]
and IEEE standards [8] periodically evaluate the scientific literature,
including the assessment of biological effects from EMF exposure generated by
telecommunication equipment. At present time, there is a consensus among such
authoritative organizations that a clear causal correlation between exposure
from mobile networks adhering to international exposure guidelines and
emergence of long-term health diseases has not been observed so far.
Consequently, 5G exposure does not pose any evident risk on the population
health. Very frequently, however, the dispute about 5G exposure is dominated
by the bias of non-scientific communities [9], who associate the exposure of
5G Radio Base Stations with severe health diseases - a connection that is not
(presently) proven by science. As a result, the installation of new 5G RBSs
over the territory is (sometimes) fiercely opposed by local communities and
advocacy groups, who act against the (supposed) increase of exposure generated
by the newly installed RBSs in their neighborhood.

Despite the exposure from 5G RBS is a matter of debate - at the extent that
the presence of a 5G antenna over a real estate has an impact on the property
value - little or no concerns are associated with 5G smartphones, which are
another (and important) source of exposure [4]. Part of the population
promptly reacts against the presence of 5G towers in proximity to their living
and working spaces, while almost nobody cares about the exposure that is
radiated by the own smartphone when uploading/downloading hundreds of
Megabytes of data through a mobile network connection. Therefore, the total
exposure levels, resulting from the combination of 5G smartphones and 5G RBSs,
are almost overlooked.

The goal of this work is twofold. On one side, we assess in a scientific way
the exposure generated by smartphones in a commercial 5G deployment. On the
other one, we compare the observed smartphone exposure levels against the ones
radiated by the serving RBS, showing that the increase of signal coverage from
5G RBS (and consequently the exposure) is highly beneficial in reducing the
EMF from the smartphone. The measurement of smartphone vs. RBS exposure has
been preliminary investigated in the context of 4G (see e.g., the very
interesting paper of Schilling et al. in [10]), but, to the best of our
knowledge, none of the previous works have conducted an in-depth measurement
analysis tailored to a 5G commercial deployment. We point out, however, that
our purpose is not to spread worries or alarms - as both smartphone and RBS
exposure naturally adhere to EMF regulations and are therefore legally safe \-
but rather to scientifically position the exposure from 5G RBSs in a wide
picture that include the contribution of 5G smartphones, the effect of
propagation conditions and the amount of traffic that is generated by User
Equipment (UE).

More concretely, we target the following questions: What is the amount of
exposure generated by a 5G smartphone and a 5G RBS in a commercial deployment?
What is the impact of uplink (UL)/ downlink (DL) traffic generated by the
smartphone on the exposure levels? How do propagation conditions (like RBS
proximity/remoteness, presence/absence of buildings on the radio link towards
the RBS) influence 5G exposure levels? How does the dual connectivity between
4G and 5G affect the exposure? The answer to these intriguing questions is the
technical goal of this paper. More specifically, our original contributions
include: i) a ground-truth overview of 5G implementation features that are
relevant for smartphone and RBS exposure assessments, with a focus on the
Italian country; ii) the definition of the measurement requirements to achieve
our goal, based on the technological features outlined in i); iii) the design
of an innovative measurement framework, called 5G Exposure Assessment (5G-EA),
which strongly leverages networking features (e.g., traffic generation &
monitoring, and remote programmability of spectrum analyzers) to satisfy the
requirements in ii); iv) the application of 5G-EA in a real 5G deployment to
collect an extensive campaign of exposure measurements.

Our results demonstrate that the smartphone exposure dominates over the RBS
one upon generation of UL traffic, especially when the UE is in Non-Line-of-
Sight (NLOS) with respect to the RBS. On the contrary, both smartphone
exposure and total EMF are reduced up to one order of magnitude when the
smartphone UL traffic traverses a radio link in Line-of-Sight (LOS) with
respect to the serving RBS. Interestingly, the exploitation of dual
connectivity feature between 4G and 5G reveals that only a small smartphone
exposure share (at most equal to 38%) is due to 5G, while the largest exposure
levels are derived from the carrier aggregation over 4G bands. Moreover, both
total and smartphone exposure-per-bit metrics are inversely proportional to
the maximum amount of UL traffic generated by the smartphone in the
measurement location, thus suggesting that innovative exposure estimators,
based on the reporting of maximum UL traffic from the smartphone, can be
designed.

Last but not least, we demonstrate that the complexity of the measurement
procedures (which need to track spatial/temporal variations of 4G carrier
aggregation and dual connectivity between 4G and 5G) can be efficiently
tackled by a framework encompassing a softwarized measurement algorithm, like
the one developed in this work. The design of softwarized-based EMF
measurement procedures, running on general purpose machines and able to
remotely control spectrum analyzers, indicate the potentials of a new market,
in which the EMF measurement algorithms are designed, shared and adopted by a
community of experts, while the manufacturers “open” the interfaces of the
measurement equipment to support the remote programmability from non-
proprietary software.

The rest of the paper is organized as follows. Related works are analyzed in
Sec. 2. Sec. 3 includes a primer about the implementation aspects of 5G
networks that are relevant to EMF monitoring, with a focus on the Italian
country - useful for the layman in the field. Sec. 4 defines the measurement
requirements, taking into account our goals and the 5G implementation aspects
of Sec. 3. The design of the 5G-EA measurement framework is described in Sec.
5. Results, retrieved from a real 5G deployment, are detailed in Sec. 6.
Finally, Sec. 7 concludes our work and reports possible future directions.

##  2 Related Work

(a) Environmental Exposure

(b) Active Traffic Exposure

Figure 1: Taxonomy of exposure measurements (figure best viewed in colors).

Fig. 1 sketches the main taxonomy of RBS and smartphone exposure measurements
over an evaluation point. More in depth, we identify the following groups:
$G1$) environmental exposure from 5G RBS, $G2$) environmental exposure from
nearby 5G smartphones, $G3$) exposure generated by the 5G RBS when a 5G
smartphone is used to inject active traffic in the evaluation point, $G4$)
exposure generated by the 5G smartphone in the same condition of $G3$.
Intuitively, groups $G1$-$G2$ identify measurements taken without injecting
any traffic in the measurement positions, and resulting in general lower
exposure levels than groups $G3$-$G4$.

In the following, we initially focus on the works tailored to the RBS side,
i.e., covering groups $G1$ and/or $G3$. Then, we focus on the works
investigating the active traffic exposure from 5G smartphone (group $G4$).
Finally, we consider the works that integrate joint measurement of
environmental/active traffic exposure from RBS and active traffic exposure
from smartphone (groups $G1$ \+ $G3$ \+ $G4$) - although we did not find any
previous work tailored to 5G. As a side comment, we intentionally leave apart
group $G2$, as the exposure contributions from nearby terminals rapidly
decrease to negligible levels when they are not in close proximity to the
evaluation point.

###  2.1 Exposure Measurements from 5G RBS

We initially focus on the works targeting: i) measurement of environmental
exposure from 5G RBS (group $G1$) [11, 12, 13, 14, 15] and ii) measurement of
active traffic exposure from 5G RBS (group $G3$) [16, 17, 18, 19, 20, 21, 22,
23, 24, 25, 26].

Focusing on $G1$ [11, 12, 13, 14, 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. Hausl et al. [15] analyze the received power over the
control channels in a 5G network by employing a code-selective measurement
methodology.

Focusing on $G3$ [16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26], Adda et al.
[16], Aerts et al. [17], Migliore et al. [18], Bornkessel et al. [19],
Schilling et al. [20], Chiaraviglio et al. [21], Liu et al. [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. The works of Aerts
et al. [24], Chountala et al. [25] and Wali et al. [26] complement the
previous ones by adding the evaluation of 5G RBS exposure when UL traffic is
forced with a terminal. In general, such works demonstrate that the exposure
from 5G RBS depends on the amount of traffic that is injected towards the
measurement location. Moreover, the active traffic contribution from the 5G
RBS is generally higher than the environmental one.

Compared to [11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26],
we tackle the 5G EMF measurement from a novel perspective, by including the
contribution of the smartphone in the EMF assessments (groups $G1$ \+ $G3$ \+
$G4$). Although we exploit some findings/intuitions of the literature (like
the idea of forcing DL/UL traffic towards the measurement location), our work
presents an innovative measurement framework, called 5G-EA, tailored to the
assessment of both smartphone and RBS EMF.

###  2.2 Exposure Measurement from 5G UE

We focus hereafter on the literature addressing active traffic exposure
measurements from 5G UE (group $G4$) [27, 28, 29, 30, 31, 32]. Xu et al. [27]
perform measurements of 5G UE power density in a semi-anechoic chamber.
Nedelcu et al. [28] analyze the UL contribution from 5G UE in terms of
radiated power. Joshi et al. [29] and Lee et al. [30] analyze 5G UE output
power levels that are collected from measurements in commercial networks.
Deaconescu et al. [31] and Miclaus et al. [32] collect EMF measurement from a
5G UE in an indoor controlled environment, with and without generating UL/DL
traffic. Overall, such works indicate that the exposure from 5G smartphones is
non-negligible, and that a huge variation in the exposure levels can be
observed. In contrast to [27, 28, 29, 30, 31, 32], in this work we go two
steps further by: i) integrating the exposure from 5G RBS and ii) performing
exposure assessments of groups $G1$ \+ $G3$ \+ $G4$ both in indoor controlled
environments and into the wild, i.e., several outdoor locations covered by a
commercial 5G RBS.

###  2.3 Joint Measurement of Smartphone and RBS Exposure

The last category relevant to our study is focused on the joint assessment of
smartphone and RBS exposure. In this case, we did not find any work covering
groups $G1$ \+ $G3$ \+ $G4$ in the 5G domain. Focusing instead on pre-5G
technologies, the most relevant work to ours is the one of Schilling et al.
[10], in which the authors propose a method based on EMF measurements to
evaluate the combined exposure from both smartphone and RBS in 4G deployments.
Interestingly, a strong reduction in the UL transmission power is observed
when the link conditions are improved. Moreover, the total exposure in a macro
cell scenario is dominated by the smartphone contribution. Eventually, the
authors advocate the need for a balance between RBS and smartphone exposure.

In line with [10], our work is also focused on the joint assessment of
smartphone and RBS exposure. However, differently from [10], we focus on a
novel domain: the 5G exposure assessment of groups $G1$ \+ $G3$ \+ $G4$, which
requires a different exposure framework than the one used by [10] for the 4G
evaluations. In addition, 5G smartphones currently employ a dual 4G/5G
connectivity to support the data transfers. Therefore, our innovative
framework evaluates the exposure over both 4G and 5G bands. This last aspects
further complicates the exposure assessment compared to [10], since multiple
4G/5G carriers are dynamically used for the data transfer.

##  3 5G Implementation Aspects Relevant to EMF Monitoring

Figure 2: Pre-5G and 5G bands in use by the W3 mobile operator.

In this section, we provide a brief overview of the key 5G implementation
aspects that have an influence the design of our EMF assessments, with a focus
on the Italian country.

Spectrum Fragmentation. 5G encompasses a wide set of spectrum portions,
including frequencies lower than 1 GHz, frequencies between 1 and 6 GHz -
a.k.a. the mid-band - and close-to-mm-Wave frequencies at around 26-27 GHz.
The most widespread option to provide 5G mobile service in Italy is the mid-
band, thanks to the fact that the adopted frequencies can guarantee the
mixture of coverage and capacity that is required during the current 5G early-
adoption phase.

The mid-band spectrum, spanning over 3.4-3.8 GHz is rather a crowded space.
Historically, the 3.4-3.6 GHz portion of the spectrum was allocated to Fixed
Wireless Access (FWA) operators [33], which provided access to household
customers over legacy technologies (pre-5G). The 3.6-3.8 GHz portion was
instead allocated with the purpose of providing 5G service for mobile
operators, with licensed spectrum blocks including both 80 MHz and 20 MHz
portions [34]. Clearly, the operators that were licensed 20 MHz of 5G mid-band
spectrum (like W3) could not support the same level of service as the one
provided by providers operating on wider bandwidth, e.g., 80 MHz. To overcome
this issue, W3 has recently signed an agreement with the FWA operator Fastweb
to lease some portions of the 3.4-3.6 GHz spectrum for the 5G mobile service
[35]. Despite the total allocation of licensed and leased bandwidth is non-
negligible (typically equal to 60 MHz for W3), the spectrum blocks for
delivering 5G in the mid-band are not contiguous. Up to this point, a natural
question is: How do such spectrum allocations affect the considered
measurements? The answer is that, for some operators (like W3), the 5G EMF
monitoring (even focusing solely on the mid-band) has to be done over multiple
not-contiguous spectrum portions. Such feature generally complicates the EMF
measurement procedure, as it is necessarly (in principle) to iterate over the
different 5G bands in use by the same operator to evaluate the total 5G
exposure.

Figure 3: Roof-top installation of W3 operator encompassing multiple sectors
and distinct panels for 4G and 5G technologies.

Interviewing of 5G and 4G networks. At the time of preparing this work, the
Non Stand Alone (NSA) option, in which the 5G radio access network is
supported by a 4G core, is still the most widespread way to implement 5G
networks in the country. Compared to a full Stand Alone (SA) deployment, NSA
requires a strong dependability of the 5G service with respect to the 4G
network. In particular, a 5G connection is always provided in parallel to a 4G
one, which acts as the anchor for the dual 4G/5G connectivity [36]. Therefore,
our EMF assessments have to include the monitoring of the 4G bands that are
used in parallel to the 5G ones, as the injected traffic is (likely) flowing
over both 4G and 5G channels. To further complicate such feature, Fig. 2
reports the band allocations of W3 over pre-5G and 5G technologies (valid for
the the city of Rome). Astonishingly, the number of possible bands that can be
used by 4G is huge, as all the spectrum portions licensed to W3 over 800-2600
MHz frequencies can be potentially used for 4G services.

Dynamicity of carrier aggregation. Another key implementation aspect that
strongly affects the EMF monitoring is the (possible) carrier aggregation
across multiple 4G bands, which are used in parallel to the 5G ones. As
reported in relevant 3GPP standards [37], there are plenty of possible carrier
aggregation combinations, ranging from sets composed of 1-2 bands up to ones
including several pre-5G spectrum portions in use by the operator. The
selected combination of aggregated carriers for a given connection is a local
decision, which depends on many features (like the propagation conditions
reported by the smartphone) that are monitored by the serving RBS.
Consequently, the adopted set of carriers cannot be determined a priori and it
depends on the measurement location. The dynamicity in the carrier aggregation
has to be taken into account in our measurement procedures, in order to limit
the exposure assessment only on those bands that are used for the transfer of
the injected traffic.

Time Division Duplexing. Fig. 2 details the assignment of frequencies for the
UL and DL directions. In different spectrum portions (B20, B8, B3, B1 and B7),
the Frequency Division Duplexing (FDD) rigidly separates the UL frequencies
with respect the DL ones. On the other hand, the B38 spectrum of 4G and all
the 5G portions in use by W3 (covering the N78 band) are employing the Time
Division Duplexing (TDD), which adopts multiplexing of both UL and DL over the
same frequencies. Intuitively, TDD complicates the dissection of smartphone
vs. RBS exposure contributions, as both time-frequency domains have to be
jointly analyzed to distinguish the DL from the RBS with respect to the UL
from the smartphone.

RBS co-location. Fig. 3 shows a typical roof-top RBS installation, which
includes multiple 5G and 4G sectors of W3 operator, as well as radio equipment
of other operators that are co-located on the same site. Since our goal is to
consider the impact of smartphone and RBS exposure for a given connection, we
need to distinguish the EMF contribution of the considered operator with
respect to the co-located ones that serve the same area (i.e., the sectors of
other operators in the figure).

##  4 5G EMF Measurement Requirements

TABLE I: 5G Features & Goals vs. EMF Measurement Requirements. Feature/Goal | Requirement
---|---
FG1 |  Operator and Technology Impact |  Narrow-band frequency-selective measurements |  R1
FG2 |  5G (and 4G) TDD |  Dissection of smartphone vs. RBS exposure |  R2
FG3 |  45/5G Dual Connectivity |  Multiple monitoring over 4G and 5G technologies |  R3
FG4 |  Spectrum Fragmentation |  Detection & iteration |
FG5 |  Dynamic carrier aggregation |  over the adopted set of carriers |  R4
FG6 |  Impact of traffic |  Forcing smartphone traffic in the UL/DL directions |  R5
FG7 |  Impact of propagation |  Selection of representative measurement locations |  R6

We analyze hereafter the measurement requirements that are instrumental for
the definition of the EMF monitoring framework, starting from the goals of our
work in Sec. 1 and the implementation features detailed in Sec. 3. To this
aim, Tab. I highlights the transition from 5G features and goals (FG1-FG7)
into concrete EMF measurement requirements (R1-R6).

More in depth, the need of distinguishing the 5G exposure contribution w.r.t.
other technologies and/or other operators (FG1) impose to adopt an approach
based on narrow-band frequency-selective measurements (R1). This is a first
and important requirement, as narrow-band measurements can be performed only
by adopting more complex instrumentation tools and procedures than the ones
used for wide-band approaches. Secondly, the adoption of TDD in 5G bands (FG2)
requires to define a measurement methodology able to dissect the smartphone
exposure contribution vs. the RBS one, which, obviously, cannot be based on
frequency separation (R2). Third, the dual connectivity between 4G/5G (FG3)
requires to perform both 4G and 5G exposure assessments (R3). Fourth, the
adoption of non-contiguous 5G portions in a fragmented spectrum (FG4), as well
as the dynamic carrier aggregation feature (FG5), suggest that the EMF
measurements should be done only on the combination of 4G/5G carriers in use
by a given connection. Therefore, rather than iterating over the whole set of
spectrum portions assigned to the operator - an operation that would result in
a waste of time and resources - a mechanism able to detect the carriers used
for a given data transfer should be designed (R4). Such feature should be
complemented by an EMF measurement procedure (possibly automated) able to
iterate over the selected set of carriers and measure the exposure on each
carrier (R4). Fifth, the impact of traffic (FG6) has to be evaluated with a
procedure able to force the traffic in UL/DL directions (R5). Finally, the
impact of propagation (FG7) can be solely assessed by selecting a
representative set of measurement locations (R6), subject to meaningful
propagation conditions (e.g., LOS/NLOS, RBS proximity/distance). It is also
clear that FG6 and FG7 inherently require that the adopted measurement chain
should be easily portable over the territory, as several measurements in
different locations should be performed in order to retrieve a meaningful set
of results.

##  5 5G-EA Framework Description

We divide the presentation of the 5G-EA framework into the following steps: i)
measured exposure metrics, ii) adopted tools and HardWare (HW) chains, iii)
description of measurement algorithm, and iv) implementation details.

###  5.1 Measured Exposure Metrics

In principle, any exposure assessment strongly depends on the target metrics
that need to be measured. In particular, the classical taxonomy defines
Specific Absorption Rate (SAR)/absorbed power density for UE assessments vs.
electric field/plane-wave power density for RBS evaluation [4]. The SAR and
absorbed power density metrics are useful when the measurement target is the
near-field assessment, in which the radiating source is (almost) attached to
the body (e.g., an UE close to the ear during a phone call). Despite such
metrics are still relevant for today equipment (and for UE SAR-based limits),
we point out that the typical smartphone user makes phone calls with the
equipment attached to the ear only to a limited extent. In fact, recent
statistics [38] reveal that smartphones are mainly used for
downloading/uploading data traffic, with the UE hold at a non negligible
distance from the head/chest in order to read/produce content on the screen.
Since our goal is to evaluate the exposure in such conditions - which
represent a typical 5G scenario - in this work we always impose a minimum
distance between the UE generating traffic and the evaluation point of our
measurement.

TABLE II: Frequencies, wavelengths, Fraunhofer regions and far field distances for the W3 bands with smartphone antenna length $L_{\text{A}}=0.08$ [m]. Frequency $f$ |  Wavelength $\lambda_{f}$ |  Fraunhofer Region $\frac{2L_{\text{A}}^{2}}{\lambda_{f}}$ |  Far-field distance $D^{\text{FF}}_{f}$
---|---|---|---
800 [Mhz] |  0.37 [m] |  0.03 [m] |  $>$0.37 [m]
900 [Mhz] |  0.33 [m] |  0.04 [m] |  $>$0.33 [m]
1800 [Mhz] |  0.16 [m] |  0.08 [m] |  $>$0.16 [m]
2100 [Mhz] |  0.14 [m] |  0.09 [m] |  $>$0.14 [m]
2600 [Mhz] |  0.12 [m] |  0.11 [m] |  $>$0.12 [m]
3447 [Mhz] |  0.09 [m] |  0.15 [m] |  $>$0.15 [m]
3547 [Mhz] |  0.08 [m] |  0.15 [m] |  $>$0.15 [m]
3610 [Mhz] |  0.08 [m] |  0.15 [m] |  $>$0.15 [m]

Apart from better matching the actual smartphone usage, the introduction of a
minimum distance between the UE and the measurement point may allow operating
in the far-field region from the UE, which is formally defined as:

$D^{\text{FF}}_{f}>\max\left(\lambda_{f},L_{\text{A}},\frac{2L_{\text{A}}^{2}}{\lambda_{f}}\right)$
(1)

where $\lambda_{f}$ is the wavelength of frequency $f$, $L_{\text{A}}$ is the
length of the radiating antenna, while the term
$\frac{2L_{\text{A}}^{2}}{\lambda_{f}}$ represents the limit of the Fraunhofer
region. To give an example, Tab. II reports the values of $\lambda_{f}$ and
$D^{\text{FF}}_{f}$ for the bandwidth allocation of W3 and a smartphone
antenna length $L_{\text{A}}$ equal to 0.08 [m]. As expected, the observed
far-field distances $D^{\text{FF}}_{f}$ strongly depend on the considered
frequencies, but, however, we can note that the minimum $D^{\text{FF}}_{f}$ is
lower than $0.2$ [m] for 4G frequencies above or equal 1800 [MHz] and for all
5G frequencies.

By imposing the distance $D^{\text{FF}}_{f}$ from the UE, we are able to
operate in far-field, a condition that is also generally experienced when
considering the RBS as the source of radiation. In this way, an homogeneous
set of metrics (e.g., electric field and/or plane-wave power density) can be
used to measure both UE and RBS exposure. This is in turn beneficial for
adopting the same measurement equipment when assessing the UE/RBS exposure, as
detailed in the following subsection.

(a) Exposure Assessment and Traffic Generation Chains (UE side)

(b) Isolation of RBS contribution for a measurement point in LOS conditions.

Figure 4: Views of the equipment tools in a given measurement location.

###  5.2 Tools and HW Chains

We describe hereafter the equipment tools, which are also sketched in Fig.
4(a). Focusing on the exposure assessment chain, we employ the following HW
($E1$-$E5$):

  1. $E1$)

Hand-held Spectrum ANalyzer (SAN) Anritsu MS2090A with maximum frequency range
equal to 32 [GHz] with 110 [Mhz] of maximum bandwidth analysis, equipped with
one battery plus another one of backup;

  2. $E2$)

Directive antenna Aaronia 6080, with frequency range 680 [MHz]-8 [GHz],
maximum gain equal to 6 [dBi], nominal impedance of 50 [Ohm];

  3. $E3$)

Coaxial cable Anritsu flexible RF 1 [m] Cable K(f)-K(m) DC-40 [GHz],
connecting the SAN to the directive antenna;

  4. $E4$)

Laptop MacBook Air with Intel Core i5 1.3 [GHz] CPU, 4 [GB] of RAM, 256 [GB]
of memory, equipped with Matlab R2017b and RSVisa 5.12.1 driver;

  5. $E5$)

Ethernet cable of 1 [m] length, Cat.5E, verified TIA-EIA-568-C.2, connecting
the laptop to the SAN.

Focusing on $E1$, the SAN allows implementing narrow-band measurements, and
thus matching requirement $R1$. Focusing then on $E2$, the directionality of
the adopted antenna allows spatially separating the contribution of the UE and
the one of the RBS. As shown in Fig. 4(b), the antenna is oriented towards the
considered source. In this way, the contribution of other sources, e.g., a UE
placed behind the measurement antenna, is not sensed. By selectively pointing
the directive antenna towards the UE or the RBS, we can isolate their
respective contributions, and thus matching requirement $R2$, even when the
monitoring is performed over TDD bands. In addition, the short coaxial cable
of $E3$ guarantees almost negligible signal degradation between the directive
antenna and the SAN \- a feature that is instrumental for measuring the
relatively low environmental exposure values of 5G. The SAN is then connected
to the $E4$ laptop via the dedicated $E5$ cable. The core of our framework is
a custom measurement algorithm written in Matlab and running on $E4$. The
algorithm allows remotely programming the SAN to perform multiple monitoring
of 4G and 5G bands (requirement $R3$), as well as to implement the automatic
detection and iteration over the adopted set of carriers (requirement $R4$).

Focusing then on the traffic generation chain, we adopt the following tools:

  1. $T1$)

Samsung S20+ 5G smartphone, equipped with Android 11 (1st May 2021) and Magic
Iperf v.1.0 App client.

  2. $T2$)

Dell Poweredge R230 server, equipped with 4 cores Intel Xeon E3-1230, 64 GB of
RAM, Ubuntu 18.04.1 OS and Iperf v.3.1.3.

More concretely, $T2$ is installed at the University building, and made
accessible through a public Internet Protocol (IP) address. In addition, the
Iperf program is used to generate synthetic traffic between $T1$ and $T2$
(either in the DL or UL direction). In this way, we accomplish requirement R5.

Fig. 4(a) shows the measurement setup in a given location. Both smartphone and
SAN are placed on tripods above around 1 [m] from ground, in order to mimic
exposure evaluations representative of users. The required setup does not
involve any electricity plug. This fact, coupled also with the overall small
size of $E1$-$E5$ and $T1$ (as shown in Fig.4(a)), as well as the availability
of a second backup battery for the SAN, allows easily repeating the
measurements in different locations of the territory, and thus matching
requirement $R6$.

###  5.3 Algorithm Description

Figure 5: High level steps of the measurement algorithm implemented in 5G-EA.
Figure 6: Steps to perform $P1$ (RBS environmental exposure).

Fig. 5 provides a high level description of the measurement algorithm
implemented in the 5G-EA framework. In more detail, we apply a divide-et-
impera approach to split the complex measurement procedure into the following
sub-problems: i) evaluation of RBS environmental exposure (step $P1$), ii)
evaluation of active traffic exposure from the smartphone (step $P2$), iii)
evaluation of active traffic exposure from the RBS (step $P3$). In addition,
the algorithm is complemented by three manual orientations $M1$-$M3$ of the
directive antenna, which are instrumental to correctly separate RBS vs.
smartphone contributions. More concretely, the directive antenna is pointed
towards the RBS before starting $P1$ ($M1$ block of Fig. 5), then it is
pointed towards the smartphone before running $P2$ ($M2$ block), and finally
it is pointed again towards the RBS before executing $P3$ ($M3$ block).

Intuitively, all the considered bands in use by the operator (TDD and DL FDD)
are swept during the environmental exposure assessment of $P1$. Then, the goal
of $P2$ is to restrict the set of monitored bands only on those ones in use by
the current active traffic connection - which is kept alive from $P2$ to $P3$.
In this way, the monitoring during $P3$ is done on the same traffic conditions
that are experienced in $P2$.

In the following, we describe in more details steps $P1$-$P3$.

Algorithm 1 Pseudo-Code of the adjust_ref_level_scale_div routine of $P1$

Input: set of SAN_settings, current frequency start curr_f_min, current
frequency stop curr_f_max, safety margin for the ref. level safety_margin,
maximum time (in s) for searching the maximum level max_time_search, pre-
amplifier state pre_amp_state, minimum level matrix min_l, number of y ticks
on the screen y_ticks
Output: ref_level, scale_div

1:set_SAN(SAN_settings);

2:max_l=-200;

3:for i=1:max_time_search do

4: max_l=max_lev_search(max_l,curr_f_min,curr_f_max);

5: sleep(1);

6:end for

7:ref_level=ceil(max_l)+safety_margin;

8:set_SAN(ref_level);

9:scale_div=abs(ref_level-min_l(curr_f_min,pre_amp_state))/ y_ticks;

10:set_SAN(scale_div);

####  5.3.1 P1 - Environmental RBS EMF

Fig. 6 shows the high level flowchart of $P1$. Initially, the set of bands to
be monitored are selected, based on the operator that is under consideration.
For example, in the case of W3 operator all TDD and DL FDD bands shown in Fig.
2 are considered for the environmental assessment of RBS exposure. The
algorithm then iterates over the set of bands. For each considered band, an
automatic procedure to adjust reference level and scale division of the
observed signal is implemented. Intuitively, the reference level is the upper
limit of the y axis in a spectrum plot (where the x axis is the set of
monitored frequencies), while the scale division allows tuning the unit of the
y ticks and consequently the lower limit on the y axis. By jointly optimizing
the reference level and the scale division, we can achieve a double goal: i)
the signal that is being monitored can be qualitatively checked on the screen
of the SAN, ii) the measurement resolution is tuned to the actual signal that
is observed, and thus the impact of (possible) measurement uncertainties is
limited.

More specifically, the adjustment of the reference level and scale division
reported in the flow chart of Fig. 6 is sketched in the
adjust_ref_level_scale_div routine of Alg. 1. This function requires as input
a set of basic SAN settings (whose values are going to be presented in detail
in Sec. 6.2.2), the starting and ending frequency for the considered band
(curr_f_min and curr_f_max), the safety_margin parameter that is used when
setting the reference level, the max_time_search parameter to govern the
maximum time for searching the maximum reference level, the pre_amp_state
boolean variable storing the state of the SAN pre-amplifier (active or
inactive), the min_l matrix including the values of the minimum sensed levels
(which depend on the adopted frequency and the pre-amplifier state) and the
y_ticks parameter representing the number of y ticks on the SAN screen.

The routine then proceeds as follows. The basic SAN settings are implemented
in line 1, which include e.g., the detector type, the measured unit, and the
trace detector. The maximum signal level max_l is initialized to a very low
value in line 2. Then, a live searching of max_l is iteratively performed in
lines 3-6, up to the maximum time max_time_search. At the end of this step,
the maximum recorded signal level is stored in max_l. The reference level
ref_level is then set by adding to max_l the safety margin in line 7. In line
8, the resulting reference level is applied. In addition, the exact scale
division, in order to entirely show the dynamics of the signal between
ref_level and min_l, is computed in line 9 and then applied to the SAN in line
10.

The execution of Alg. 1 is then iterated up to a maximum number (iterator
index in the flowchart of Fig. 6), in order to improve the setting of
reference level and scale division. In the following step, a check on the pre-
amplification is performed. If the current reference level is lower than a
pre-amplification threshold, the signal can be pre-amplified by the SAN (right
part of Fig. 6).111Reference levels higher than the pre-amplification
threshold may result into an Analog-to-Digital Converter (ADC) over-range
after activating the pre-amplification of the signal. Therefore, this feature
should be activated only for those signals whose reference level and dynamics
are within the ADC limits. Such feature is particularly useful for the
environmental assessment of 5G signals, which are normally very low and close
to the equipment noise level, due to a relatively low usage of 5G on such
early phase of adoption. After turning on the pre-amplifier, the
adjust_ref_level_scale_div routine is called again, in order to adjust the
amplified signal levels. However, this procedure may increase the reference
level again above the maximum one allowed by the pre-amplifier. Consequently,
a check on the pre-amplification threshold is done again. In case pre-
amplification can be kept turned on, a further adjustment of reference level
and scale division is done - and a further check on the pre-amplification is
performed. In case pre-amplification is not supported, the pre-amplification
is turned off, the reference level and scale division are reverted back to the
last values before pre-amplification, and (eventually) a further call of the
adjust_ref_level_scale_div routine is run.

Algorithm 2 Pseudo-Code of the nar_band_meas routine of $P1$, $P2$ and $P3$

Input: set of basic SAN_settings, current frequency start curr_f_min, current
frequency stop curr_f_max, number of samples n_samples, inter sample time (in
s) int_sample_time
Output: Array of exposure values curr_exp in dBm/m2 ($P1$) or V/m ($P2$ and
$P3$)

1:set_SAN(SAN_settings);

2:for i=1:n_samples do

3: curr_exp(i)=get_SA(curr_f_min,curr_f_max);

4: sleep(int_sample_time);

5:end for

After setting reference level and scale division (and eventual activation of
pre-amplification), the signal is ready to be measured. To this aim, a narrow
band measurement, expanded in Alg. 2, is invoked. The function takes as input
a set of basic SAN settings, the current frequency start curr_f_min and
frequency stop curr_f_max, the number of sampled channel power measurements
n_samples, and the inter-sample time int_sample_time. The routine then
produces as output an array of exposure values curr_exp. The logic of the
procedure is very simple: after setting the SAN parameters, a channel power
computation function is iteratively invoked on the SAN. When all the samples
are recorded, the algorithm returns the array of exposure measurements
curr_exp. At the end of $P1$, the RBS environmental exposure is measured for
the set of bands in use by the operator.

####  5.3.2 P2 - Active Traffic Smartphone EMF

The goal of the second part of the algorithm is to perform the assessment of
the exposure generated by the smartphone upon active traffic generation. To
this aim, the dual 4G/5G connectivity and carrier aggregation features suggest
that multiple bands (unknown a-priori) can be used in parallel for the data
transfer. On the other hand, measuring the exposure on the entire set of bands
in use by the operator may result in a waste of resources, in terms of: i)
overly increase of time to perform the assessment, ii) waste of consumption of
the SAN battery (which is a precious resource) and iii) excessive traffic
consumption on the smartphone (which may be critical for limited data traffic
plans). To face such issues altogether, $P2$ adopts the following intuition.
First, the TDD and UL FDD bands in use by the data transfer are detected.
Then, the exposure assessment is done only on the selected subset of spectrum
portions currently in use.

Figure 7: Steps to perform $P2$ (active traffic smartphone exposure).

To this aim, Fig. 7 sketches the main operations performed during $P2$.
Initially, a wide span assessment is done, in order to detect the peak(s) of
the sensed signals on a very large range of frequencies (including all the
ones in use by the operator under investigation). The goal of this scan is not
to measure exposure, but rather to get a quick indication on the frequencies
that carry most of signal power before injecting any traffic. In the following
step, the active traffic is generated towards the smartphone, by executing the
Iperf program. Then, a second wide span assessment is done. The detection of
the subset of 4G/5G bands in use for the data transfer is done by comparing
the peaks recorded during the first scan vs. the ones observed on the second
one.

Algorithm 3 Pseudo-Code of the sel_band_use routine of P2

Input: array of EMF values from 1st span emf_array_1st_span, array of EMF
values from 2nd span emf_array_2nd_span, array of frequency values freq_array,
threshold increase parameter thre_inc
Output: array of selected bands sel_band_array

1:sel_band_array=initialize();

2:for f=1:size(freq_array) do

3: if incr_percent(emf_array_2nd_span_rev(f), emf_array_1st_span(f)) $>$
thre_incr then

4: band_index=find_b_index(f);

5: sel_band_array(band_index)=1;

6: if fdd_array(band_index)==1 then

7: band_index_fdd=find_b_index(f,FDD);

8: sel_band_array(band_index_fdd)=1;

9: end if

10: end if

11:end for

The detection of the 4G/5G bands is expanded in Alg. 3. The routine takes as
input the emf_array_1st_span array of EMF values (indexed by frequency) that
were sensed during the first span, the emf_array_2nd_span array of EMF values
that were sensed during the second span, and a threshold increase parameter
thre_inc (in %) to activate the detection. The algorithm then produces as
output the subset of bands sel_band_array that are detected for the current
data transfer. The logic of the function is quite simple: for each considered
frequency, the sample in emf_array_2nd_span is compared against the
corresponding one emf_array_1st_span, by computing the percentage variation of
EMF. If such variation is greater than the thre_inc parameter, the band is
included in the list of spectrum portions that are monitored for the current
data transfer. The intuition here is in fact to exploit the increase of EMF as
a result of the usage of specific bands in the UL. In case the current
selected band employs FDD, then the corresponding one in the DL is also
included in the list of selected ones. For example, let us assume that the
1745-1765 [MHz] band of Fig. 2 is detected in the UL. This portion of the
spectrum belongs to the B3 FDD band, which also includes the 1840-1860 [MHz]
band for the DL. This second portion will be likely used when evaluating the
active traffic from the RBS, and therefore it is included in the list of bands
to be monitored - when considering RBS active traffic exposure. At the end of
the algorithm the array sel_band_array stores the list of band indexes in use
for the current data transfer.

Coming back to the flowchart of Fig. 7, the blocks on the right details the
steps for the EMF assessment on the selected set of bands. In particular, the
initial band is selected - the index in sel_band_array with lowest frequency
and belonging to UL. Then, the narrow-band EMF measurement on the selected
band is performed. The logic is in common with the exposure measurement of
$P1$, and sketched in Alg. 2. In particular, the main differences rely on a
different set of basic SAN settings and on a different measurement metric (in
terms of V/m). Once the measurement has been completed for the current band,
$P2$ passes to the next one, until all the TDD and UL FDD bands in
sel_band_array are considered (band index in Fig. 7). At the end of $P2$, a
set of exposure arrays, one for each considered band, is available.

Figure 8: Steps to perform $P3$ (active traffic RBS exposure).

####  5.3.3 P3 - Active Traffic RBS EMF

The last part of our measurement technique involves the assessment of RBS
exposure while keeping active the current data transfer. Fig. 8 highlights the
main blocks that realize this functionality. The logic is very similar to the
smartphone assessment, except for the following differences: i) the evaluation
include TDD and FDD DL bands (with the directive antenna pointed towards the
RBS), ii) the smartphone traffic is turned off after completing the scan over
the considered band. Similarly to $P2$, a set of exposure arrays is available
at the end of the procedure.

###  5.4 Implementation Details

We implement $P1$-$P2$-$P3$ parts of 5G-EA framework as a set of scripts
written in Matlab - except from the traffic generation, which is governed by
the Iperf program running on the smartphone and dedicated server. An unique
aspect of our framework is the implementation of the measurement algorithm in
software, on a general purpose machine that controls the SAN. This is another
innovation brought by our work, which opens the way for possible future
investigation in the softwarization of EMF assessments.

More technically, the high level functionalities reported in $P1$-$P2$-$P3$
are translated into a set of basic operations, coded as low-level Standard
Commands for Programmable Instruments (SCPI) and transfered from/to the SAN
through a Transmission Control Protocol (TCP) connection. The output of the
SAN (e.g., the array including the exposure values) are then sent back over
the same connection in SCPI format. In this way, the process is completely
automated and the the post-analysis of the obtained data can be done directly
in Matlab - in the same script running the measurement algorithm.

##  6 Results

We present our outcomes through the following steps: i) description of
evaluation scenarios, ii) parameter settings of 5G-EA framework, iii) exposure
assessments.

###  6.1 Evaluation Scenarios

Figure 9: Positioning of W3 tower and outdoor LOS (L green square) and NLOS (N
blue square) measurement locations (map source: Google Maps) - figure best
viewed in colors.

We consider a set of measurement points in the coverage area of the W3 roof-
top installation shown in Fig. 3, with the frequency assignment reported in
Fig. 2. The installation is located in the area close the University of Rome
Tor Vergata in Rome (Italy). More concretely, we perform our experiments in
both outdoor and indoor locations, due to the following reasons. First, we aim
at massively performing measurements under different propagation conditions,
which are obviously influenced by terrain parameters like the distance from
the RBS, the level of urbanization around the measurement point and the
presence of buildings/obstacles on the path towards the RBS. Second, we
exploit the indoor locations to perform detailed and in-depth measurements,
with the goal of corroborating the results from the outdoor locations with
tests covering e.g., sensitivity analysis of the exposure vs. variation key
parameters, such as throughtput, distance from the smartphone, and smartphone
orientation.

Focusing on the outdoor tests, Fig. 9 reports a 3D map of the measurement
locations. In total, 26 measurement locations are selected for the tests,
based on the following criteria: i) spreading the tests over the territory
around the W3 tower, and ii) finding locations that are suitable for placing
the instruments (e..g., avoid private streets, locations in close proximity to
each other, etc.). The 3D distance of each measurement location from the RBS
varies between a minimum of 124 [m] up to a maximum of 1134 [m],222The
percentage difference between ground (2D) distance and 3D one is always
smaller than 2%. Consequently, both distances almost overlap. in order to
capture a wide set of exposure conditions. In addition, the measurement points
are placed in the coverage area of each W3 sector shown in Fig. 3, in order to
further strengthen our analysis. We refer the reader to Appendix A, which
provides detailed information about: i) radio configuration of the W3
installation under consideration, ii) other RBSs in the surroundings of the
considered area, iii) taxonomy of outoor locations, iv) measurement time.

(a) LOS Location - Engineering Parking

(b) NLOS Location - Church Square

Figure 10: Example of LOS and NLOS outdoor measurement locations.

To give more insights, Fig. 10 reports two representative examples of outdoor
measurement points. When considering LOS locations (Fig. 10(a)), the RBS is
visible from the measurement point. On the contrary, the installation is not
visible in NLOS locations (Fig. 10(b)). In both cases, the directive antenna
is pointed towards the RBS location during operations $M1$ and $M3$.

(a) Positioning of LOS and NLOS indoor locations

(b) View of LOS indoor location

(c) Window view from LOS location

(d) Window view from NLOS location

Figure 11: LOS and NLOS indoor measurement locations.

Focusing then on the indoor tests, we consider two locations at the
Engineering building of the University, shown in Fig. 11(a). In particular, we
consider a LOS location at the fourth floor and a NLOS one at the second one.
The room hosting the LOS measurements is shown in Fig. 11(b). In addition, the
environment hosting the NLOS tests is identical to the LOS one (not shown due
to the lack of space). Interestingly, the walls are made of thin concrete
pillars and big glasses that are mounted on small metallic frames. This
structure provides in general good penetration of outdoor mobile signals
inside the building. The window view from both locations is shown in Fig.
11(c)-Fig. 11(d). Focusing on the LOS environment, the path towards the RBS is
free from obstacles and the distance is within the RBS coverage area. Focusing
instead on the NLOS location, the distance from the RBS is identical to the
LOS one, but, obviously, the RBS sight is obstructed by a building, which
forces the signal to follow a NLOS path.

###  6.2 Parameters Settings

We provide herefater the settings of the main parameters of 5G-EA framework.
In particular, we shed light on the measurement antenna positioning and the
algorithm parameters in $P1$, $P2$, and $P3$, respectively. We refer the
reader to Appendix B for more insights about calibration and uncertainty
aspects of the measurement chain.

Figure 12: Occurrence of bands used for the 4G/5G data transfers in the
considered scenarios

####  6.2.1 Measurement Antenna Placement

5G-EA requires a careful orientation and positioning of the measurement
antenna, in order to properly dissect RBS vs. smartphone exposure. Focusing on
$M1$ and $M3$, the antenna is simply pointed towards the RBS location.
Focusing on $M2$, the antenna is pointed towards the smartphone. As already
shown in Tab. II, the far-field distance $D_{f}^{\text{FF}}$ has to be
enforced in our experiments, in order to avoid near-field effects. On the
other hand, the distance should mimic the actual exposure conditions to the
head/chest that is experienced by a typical user. Therefore, there is trade-
off between the (small) distance for a meaningful assessment and the
(relatively large) distance that has to be enforced to preserve far-field
conditions. In this work, we have found that a good compromise among such
competing goals can be achieved by setting a distance from the smartphone
equal to 0.25 [m]. Although this number may apparently violate the far-field
conditions for the 800 [MHz] and 900 [MHz] bands (as shown in Tab. II), in
practice we have found that such bands are not used for 4G data transfers.

To corroborate the previous outcome, Fig. 12 reports the occurrence of bands
that are used for the data transfers in the outdoor locations of our
experiments. Interestingly, most of transfers employ 4G bands at around 2600
[MHz], while the 1800 [MHz] band is seldom used. On the other hand, the 800
[MHz] and 900 [MHz] bands are not used by the data transfers. Eventually, all
the three 5G bands at 3.4-3.6 [GHz] are almost equally adopted. In this way,
the minimum frequency used for the exposure assessment can be assumed to be
the 1800 [MHz], band. Therefore, the distance of 0.25 [m] is sufficiently
large to provide far-field conditions.

TABLE III: Parameters of adjust_ref_level_scale_div routine ($P1$). | Parameter | Value/Setting
---|---|---
| Unit | dBm/m2
| Attenuation | Auto
| Resolution Bandwidth | Auto
| Video Bandwidth | Auto
| Number of sweep points | Auto
| Trace Detector | Root Mean Square (RMS)
| Type Detector | Rolling Max
| Initial Reference Level | 65 dBm/m2
SAN_settings | Initial Scale/Div | 15
| curr_f_min | Fig. 2 (FDD DL, TDD)
| curr_f_max
| pre_amp_state |  Fig. 6
| min_l |  Tab. IV
| pre_amp_thre |  -48.77 dBm/m2
| safety_margin |  10 dBm/m2
| max_time_search | 5 s
Param. Settings | y_ticks | 10
TABLE IV: min_l values as a function of frequency and pre-amplifier state. | min_l
---|---
curr_f_min |  pre_amp_state=0 (inactive) |  pre_amp_state=1 (active)
791, 832, 950, 905 [MHz] |  -100 [dBm/m2] |  -115 [dBm/m2]
1840, 1745, 2110, 1920 [MHz] |  -95 [dBm/m2] |  -107 [dBm/m2]
2670, 2550, 2570 [MHz] |  -90 [dBm/m2] |  -103 [dBm/m2]
3437, 3537, 3600 [MHz] |  -85 [dBm/m2] |  -97 [dBm/m2]

####  6.2.2 $P1$ Parameters

Tab. III reports the parameters of the adjust_ref_level_scale_div function. In
more detail, the upper part of the table expands the basic SAN parameters. In
particular, we adopt a rolling max type detector, as our goal here is to first
sense the maximum signal levels and then adjust accordingly reference level
and scale division. Focusing on the other routine parameters (bottom part of
the table), the values of curr_f_min and curr_f_max are taken from Fig. 2, by
considering the W3 bands over FDD DL and TDD \- since the exposure from RBS is
the target of $P1$. In addition, the pre-amplifier state (on/off) is governed
by the logic reported in Fig. 6. Obviously, the pre-amplifier is inactive when
the first call of adjust_ref_level_scale_div is run (left part of Fig. 6).
However, in case the pre-amplification management branch is followed (right
part of Fig. 6), the pre_amp_state state that is passed to
adjust_ref_level_scale_div may be active.

Focusing on the remaining parameters of adjust_ref_level_scale_div, the min_l
matrix is reported in Tab. IV. The values reported in the table are retrieved
by visualizing the noise level on the SAN in each considered band. Clearly,
when the pre-amplifier is turned on, the noise level can be notably reduced
(right part of the table). Moreover, the pre_amp_thre threshold, which is used
in the decision block of Fig. 6 to compare the reference level and
activatate/deactivate the pre-amplification, is set to -48.77 dBm/m2 (a
setting that depends on the SAN HW and the features of the directive antenna).
In addition, the safety_margin parameter is set to 10 [dBm/m2] - an empirical
value that was tested to correctly work on all the considered bands. Finally,
the maximum time for searching the signal peak over the considered band is set
to 5 [s], while the number of y ticks is set to y_ticks=10. In this way, the
time required to run a single call of adjust_ref_level_scale_div is at least
equal to 5 [s].

TABLE V: SAN and parameter settings for the nar_band_meas routine of $P1$, $P2$ and $P3$. |  | Value/Setting
---|---|---
|  Parameter |  |
|  | $P1$ |  $P2$, $P3$
|  Unit | dBm/m2 | V/m
|  Attenuation | Auto
|  Resolution Bandwidth | Auto
|  Video Bandwidth | Auto
|  Sweep points | Auto
|  Trace Detector | Root Mean Square (RMS)
|  Type Detector | Rolling Average
|  Avg. samples | 100
|  Reference Level |  Set by the logic in Fig. 6 |  6 V/m
SAN_settings |  Scale/Div |  Set by the logic in Fig. 6 |  Automatic
|  curr_f_min |  Fig. 2 (FDD DL, TDD) |  Extracted from
|  curr_f_max |  sel_band_array
|  n_samples | 12
Parameters |  int_sample_time | 0.5 s

Tab. V reports the parameters for the nar_band_meas function. Focusing on the
$P1$ settings (central column), the set of SAN_parameters this time includes a
rolling average as type detector, as our primary goal during this step is to
perform an exposure assessment over the considered signal. This setting is
inline with relevant measurement standards in the field (see e.g., [39]). In
addition, the number of samples for computing the average is set to 100, in
order to consider a meaningful range. Clearly, the reference level and the
scale division are updated by the logic implemented in Fig. 6. Focusing then
on the remaining parameters, curr_f_min and curr_f_max are set in accordance
to the set of W3 bands shown in Fig. 2 (restricted to FDD DL and TDD).
Finally, the number of narrow band measurements n_samples is set to 12, while
the time between consecutive queries of narrow band measurements is set to 0.5
[s]. In this way, the measurement time for each band is approximately equal to
12 $\times$ 0.5 [s] = 6 [s].

Finally, we shed light on the remaining parameters that appear in Fig. 6.
Focusing on the maximum number of iterations for running the
adjust_ref_level_scale_div function, we set it to 3 - a value that provides a
good balance between overall duration of detection phase and precision in
setting reference level and scale division values. Focusing on the number of
bands to be monitored, we set it to 9, in accordance to the FDD DL and TDD
bands of W3 shown in Fig. 2. With such settings, the required time to run $P1$
is at least equal to (5 [s] $\times$ 3 + 6 [s]) $\times$ 9 = 189 [s]. However,
the actual total time for running $P1$ may be higher, due to the following
reasons: i) the additional delay that is required when communicating with the
SAN and ii) the eventual activation of the power amplifier, which requires
further calls of the adjust_ref_level_scale_div routine.

####  6.2.3 $P2$ Parameters

TABLE VI: SAN settings for wide span measurements $P2$. Parameter | Value/Setting
---|---
Unit | V/m
Attenuation | Auto
Pre-amplifier | Off
Frequency Start | 791 MHz
Frequency Stop | 3620 MHz
Resolution Bandwidth | Auto
Video Bandwidth | Auto
Sweep Points | Auto
Trace Detector | Root Mean Square (RMS)
Type Detector | Max. Detector
Reference Level | 6 V/m
Scale/Div | Automatic

We initially consider the parameters of the wide span measurement blocks
reported in Fig. 7, detailed in Tab. VI. More in depth, most of SAN parameters
are set to the default values (i.e., automatic setting). Focusing on the
remamining parameters, the selected frequencies cover all the ones in use by
W3 operator. In addition, the pre-amplifier is powered off, as the exposure
from the smartphone may be potentially higher than the maximum supported
signal level with pre-amplification turned on - which we remind is a very
effective feature to distinguish low signals w.r.t. noise level. Moreover, the
reference level is set to a large value (6 [V/m]) in order to detect possible
signal peaks. Focusing on the trace detector, a root mean square (RMS) setting
is imposed (in line with $P1$). Eventually, the max detector is used, as we
remind that the scope of the wide span measurement is to perform a quick scan
over the entire frequency range and to detect the signal peaks.

The second block of $P2$ is the activation of the smartphone traffic exposure.
Unless otherwise specified, the iperf client is run on the smartphone with the
following parameters: i) IP address and port corresponding to the iperf server
installed at the University, ii) bandwidth report interval set to 1 [s], iii)
number of simultaneous connections per data transfer set to 1, iv) maximum
duration of iperf transfer set to 120 [s] - an amount of time sufficiently
large to complete the remaining steps in $P2$ and $P3$.

In the following, we focus on the parameters for selecting the 4G/5G bands in
use by the data transfer (expanded in the sel_band_use routine of Tab. 3).
Obviously, the array of EMF measurements are set by the first and second wide
span assessment. The array of frequency values freq_array includes all the
frequencies in use by W3 operator. Finally, the treshold increase parameter
thre_inc is set to 30% - a value that guarantees a good balance between
(artificial) increase of EMF due to injection of traffic from the smartphone
and (natural) variation of exposure due to other effects (e.g., nearby
terminals, signal fading, etc.).

(a) Exposure Breakdown

(b) Throughput Breakdown

Figure 13: Characterization of exposure (top) and throughput (bottom) over the
outdoor evaluation points. The points are ordered from left to right by
increasing distance values with respect to the 5G W3 installation. The sight
condition (L=Line-of-Sight, N=Non-Line-Of-Sight) is also reported.

Eventually, the band index in Fig. 7 is initialized with the first UL
bandwidth in which an increase of exposure was detected by the sel_band_use
routine. Finally, the narrow band EMF measurement is realized through the
nar_band_meas of Alg. 2, whose parameters for $P2$ are detailed in Tab. V on
the right. The main difference w.r.t. $P1$ case relies on a different
measurement metric, expressed in terms of [V/m]. In addition, the reference
level set to 6 [V/m], since the measured signal levels are expected to be non-
negligible. It is interesting to note that, when the [V/m] metric is set, the
scale div are automatically tuned to show a minimum level of 0 [V/m], i.e.,
the minimum one. Eventually, the minimum and maximum frequency are set in
accordance to the considered bandwidth, whose set is saved in the
sel_band_array by the sel_band_use routine.

####  6.2.4 $P3$ Parameters

Focusing on the $P3$ assessment shown in Fig. 8, the initial band index is set
with the first DL bandwidth of W3 in use by the data transfer (detected by
sel_band_use function). The narrow band assessment reported in the second
block of $P3$ adopts the same parameters of $P2$ (detailed in Tab. V on the
right). Finally, the iperf transfer is turned off on the smartphone when all
the bands in use have been considered.

###  6.3 Exposure Assessments

We initially concentrate on the outcomes from outdoor measurements of Fig. 9
and then we shed light on the results obtained in the indoor locations of Fig.
11.

####  6.3.1 Outdoor measurements

We run 5G-EA over the outdoor locations, by considering the generation of UL
traffic from the smartphone to the iperf server. Fig. 13 reports the breakdown
of exposure (top) and throughput (bottom). The exposure is expressed in terms
of power density [W/m2], in order to display the different contributions (RBS
vs. smartphone, active vs. environmental, pre-5G vs. 5G) over a stacked bar.
Each exposure component is expressed in terms of average value over the
collected samples. Moreover, the error bars report the confidence intervals,
which are computed by assuming a Gaussian distribution with a confidence level
of 95%.

We initially focus on the collected exposure values, shown in Fig. 13(a).
Several considerations hold by analyzing in detail the figure. First, the
active traffic exposure from the smartphone (pre-5G and 5G) dominates over all
the other ones, in all the considered locations. Second, the RBS environmental
exposure can be identified for all locations in LOS w.r.t. the RBS, while the
same metric is negligible for all locations in NLOS. Third, the contribution
of active traffic exposure from the RBS is almost imperceptible in all
locations (except from two). Fourth, the majority of active traffic exposure
from the smartphone is due to pre-5G contributions (mostly 4G), while 5G
always represents a small share (at most equal to 38%) compared to the total
one that is radiated by the smartphone. Fifth, NLOS locations generally
present higher level of 5G exposure than LOS ones. Sixth, the increase of
distance generally results in an increase of exposure (left to right of the
figure). However, the largest exposure variations are observed between LOS and
NLOS evaluation points. In particular, the latters exhibit a strong increase
of active traffic exposure from the smartphone compared to the formers. As a
side comment, the measured expsure levels are always orders of magnitude lower
than the whole body and localized maximum power density values of ICNIRP
guidelines [6].

In the following step, we compare the exposure of Fig. 13(a) against the
achieved throughput levels shown in Fig. 13(b). Interestingly, a strong
variation in the throughput levels is observed. We argue that this phenomenon
is due to the different propagation conditions that are experienced in the
measurement locations. To substantiate such observation, Fig. 13(a) highlights
the three locations exhibiting the lowest exposure and the other three ones
providing the highest exposure levels. Interestingly, the formers are in LOS,
while the latters experience NLOS. When considering the throughput metric for
the same locations (Fig. 13(b)), we can note that locations with lowest
exposure (LOS) achieve very large throughput levels, typically larger than 45
[Mbps] in the UL, while the opposite holds for locations experiencing the
highest exposure levels (NLOS), being the observed throughput lower than 16
[Mps]. Consequently, NLOS conditions are reflected into an increase of
smartphone exposure and a degradation of throughput levels compared to LOS
ones.333We refer the interested reader to Appendix C for more speculations
about the variations of exposure for smaller distances than the minimum one
considered in this work.

(a) Percentage of Smartphone Exposure vs. UL Throughput

(b) Smartphone Exposure-per-Mbps vs. UL Throughput

Figure 14: Incidence of UL throughput on the smartphone active traffic
exposure percentage (top) and on exposure-per-Mbps (bottom)

To provide more insights, Fig. 14(a) reports the percentage of active traffic
exposure from the smartphone (w.r.t. the total one) vs. the observed
throughput level. Each point in the figure corresponds to a measurement
location (distinguished between LOS and NLOS), while x-y error bars are
computed by assuming again 95% of confidence levels. Interestingly, we can
note that the percentage of smartphone exposure is huge (close to 100%) for
all the NLOS measurement locations. On the contrary, the percentage of
smartphone exposure tends to decrease to lower levels for the LOS measurement
locations. Moreover, a decrease is also observed when the realized UL
throughput increases. In all the cases, however, the active traffic exposure
from the smartphone is always higher than 50%, thus representing the largest
source of exposure.

Having understood that there may be a strong relationship between the realized
UL throughput and the collected exposure levels, we compute a novel metric,
called smartphone exposure-per-Mbps, which is obtained by dividing the total
exposure measured in the location by the observed throughput. The metric
expresses the efficiency in terms of exposure (in [V/m]) for delivering a
given amount of information (in [Mbps]). When the exposure-per-Mbps is high,
the system is largely inefficient, as a huge exposure is needed to transfer
the information. On the contrary, when the exposure-per-Mbps is low, the
efficiency of the system in delivering the same amount of information is
improved.

TABLE VII: Fitting parameters for the exposure estimator. Parameter | Value
---|---
$F_{1}$ | 1.146 [V/m/Mbps]
$E_{1}$ |  -0.2595 [Mbps-1]
$F_{2}$ | 0.1304 [V/m/Mbps]
$E_{2}$ |  -0.0325 [Mbps-1]

Fig. 14(b) reports the smartphone exposure-per-Mbps vs. the observed
throughput levels. Interestingly, the exposure-per-Mbps is inversely
proportional to the throughput levels. The higher is the throughput, the
flatter and closer to zero is the observed smartphone exposure-per-Mbps. On
the contrary, the lower is the throughput, the higher is the asymptotic
behavior of the exposure-per-Mbps, with the highest values observed for the
lowest throughput levels. To better capture the aforementioned effects, we
have applied the following double exponential fitting model:

$C^{\text{EST}}=F_{1}\cdot e^{E_{1}\cdot T^{\text{UL}}}+F_{2}\cdot
e^{E_{2}\cdot T^{\text{UL}}}$ (2)

where $T^{\text{UL}}$ is the observed throghput level (in Mbps), $F_{1}$,
$E_{1}$, $F_{2}$, $E_{2}$ are the fitting parameters (shown in Tab. VII) and
$C^{\text{EST}}$ is the estimated smartphone exposure-per-Mbps.

Figure 15:  LOS/NLOS indoor locations: breakdown of exposure components vs.
variation of UL/DL throughput.

By observing in detail Fig. 14(b) we can note that the realized UL throughput
with iPerf tool can be used as an estimator of the smartphone exposure. In a
practical scenario, the user could measure $T^{\text{UL}}$ by running an iPerf
test in the UL direction and a given location. Then, the smartphone exposure
could be retrieved by: i) applying the fitting model of Eq. (2) to compute
$C^{\text{EST}}$, ii) computing the total estimated exposure as
$C^{\text{EST}}\times T^{\text{UL}}$. Clearly, the values reported in Tab. 2
may depend on different metrics (like the smartphone model), whose impact on
the fitting model (and consequently on the exposure levels) require further
investigations as future work.444The adoption of additional metrics (derived
e.g., from control channels) to predict exposure levels is discussed in
Appendix D.

(a) Total EMF vs. throughput

(b) Total Exposure-per-Mbps vs. throughput

Figure 16: LOS/NLOS indoor locations: total EMF and total exposure-per-Mbps
vs. throughput.

####  6.3.2 Indoor measurements

In the following part of our work, we extend the results of the outdoor
locations by investigating the exposure in the LOS/NLOS indoor locations. In
particular, the availability of controlled environments allows performing
extensive tests, in order to deeply analyze the impact of key parameteres on
the exposure levels. To this aim, we initially focus on the impact of UL vs.
DL traffic generation. For each location, we perform a wide range of UL and DL
tests, including the variation of the generated traffic from very low values
(set to 5 [Mbps]) up to the maximum one reachable on the wireless link
(several dozens of Mbps). Moreover, three independent runs are executed for
each parameter setting, in order to strengthen our outcomes.

Fig. 15 reports the breakdown of the exposure components for the considered
tests. Four considerations hold by analyzing the figure. First, the exposure
generated by the smartphone on pre-5G technologies dominates over the other
components, both in the DL and in the UL. Second, the exposure in the DL is
almost one order of magnitude lower than the one recorded in the UL tests.
This is due to the fact that DL tests generate most of traffic flows from the
Iperf server to the client, while a very low amount of information flows on
the inverse direction (e.g., segment acknowledgements and/or control
information). Third, the exposure tends to increase when the UL throughput is
increased.555This outcome, which appears apparently in contrast to the results
of the outdoor locations, is instead a consequence of manual traffic setting
and stability in propagation conditions that are experienced by the indoor
tests of the same location, as deeply analyzed in Appendix E. Fourth, the
exposure tends to be higher in the NLOS location than the LOS one, for a given
level of generated traffic.

To better substantiate the previous findings, Fig. 16(a) details the total EMF
vs. throughput over the two indoor locations. Interestingly, a strong increase
of exposure is recorded when the UL traffic is increased, easily reaching
values greater than 1 [V/m] (top part of the figure). Moreover, the difference
between NLOS and LOS exposure tends to increase with increasing UL throughput.
At last, when Iperf is set to generate the maximum traffic, the UL throughput
in the NLOS location is clearly lower than the LOS one - despite the fact that
the exposure values are comparable in both locations. A similar behaviour is
also observed for the maximum traffic in the DL direction (bottom part of the
figure). However, the increase of exposure due to traffic rising is less
evident than in UL tests.

(a) Smartphone orientation variation with respect to the base station

(b) EMF evaluation distance variation from the smartphone

Figure 17: Impact of smartphone orientation and EMF evaluation distance from
the smartphone - LOS location with maximum UL traffic setting.

.

Eventually, Fig. 16(b) reports the total exposure-per-Mbps vs. throughput for
the two locations and the UL/DL tests. Astonishingly, the inversely
proportional law between exposure-per-Mbps and throughput clearly emerges in
each location and in each direction. Given a location, the total exposure-per-
Mbps is lower for the DL tests compared to the UL ones. Morever, given a
direction of traffic generation, the total exposure-per-Mbps in NLOS tends to
be higher than the one recorded in the LOS condition.

In the final part of our work, we evaluate the impact of changing the
orientation and relative positioninig of the smartphone. Fig. 17(a) reports
the exposure breakdown for each smartphone orientation setting reported on the
bottom of the figure, corresponding to a smartphone rotation of $0^{\circ}$,
$90^{\circ}$, $270^{\circ}$ in clockwise direction of the horizontal plane.
For each angle, we perform three independent assessment with the maximum UL
traffic. Interestingly, the dominance of smartphone exposure is evident in all
the experiments. However, the $270^{\circ}$ rotation generally results in an
higher exposure from the smartphone than the other angles. We argue that this
phenomenon is due to the positioning of the smartphone main antenna on bottom
right part the smartphone - opposite to the RBS when an angle of $270^{\circ}$
is set, which results in worse propagation conditions than the other settings.

Finally, we analyze the impact of increasing the distance of the exposure
evaluation point w.r.t. the smartphone, as shown in Fig. 17(b). Starting from
the default value of 0.25 [m], we increase the distance up to 1 [m]. As
expected, the exposure experiences a sharp decrease when the distance is
increased. However, we point out that the smartphone exposure is still the
dominant component even when the distance is set to the maximum value of 1
[m].

##  7 Conclusions and Future Work

We have investigated the problem of exposure assessment in a commercial 5G
network, by evaluating the impact of user generated traffic on the exposure
from the RBS and the smartphone. To solve the complex and innovative
measurement requirements - which include several aspects related to 5G
implementation and its inter-viewing with legacy 4G networks - we have
designed an innovative framework, called 5G-EA. Our framework splits the
complex problem into a set of procedures, which are tailored to a specific
measurement goal (environmental vs. active traffic assessment). In addition,
5G-EA relies on a completely softwarized approach, in which the measurement
algorithm is run on a general purpose machine that controls the programmable
spectrum analyzer.

We have then performed an extensive set of assessments in both outdoor and
indoor locations. Interestingly, our results demonstrate that the smartphone
contribution largely dominates over the other exposure components,
particularly when UL traffic is injected. However, the largest contribution is
due to pre-5G technologies, while 5G always constitute a small share (up to
38%) out of the total one that is radiated by the smartphone. In addition, the
total exposure dramatically decreases when outdoor LOS conditions are
experienced, and in general when the exposure from the RBS becomes detectable
by the SAN. Moreover, we have designed and evaluated an exposure estimator
based on the maximum UL traffic that is achieved by iPerf in the measurement
location. Eventually, the exposure tends to increase in indoor locations when
passing from LOS to NLOS condition, for a given level of UL traffic that is
set towards the smartphone. Finally, we have demonstrated that the measured
exposure levels are influenced by key parameters, like DL vs. UL direction,
smartphone orientation and relative distance of the smartphone w.r.t. the
measurement antenna.

We believe that our work paves the way for future research in the field.
First, the application of 5G-EA in other deployments (e.g., subject to
different exposure regulations and/or different radio configurations) is an
interesting step. Second, the evaluation of exposure should be extended by
considering multiple UE models/types, locations in balconies/terraces in close
proximity to the serving RBS, and additional sources like WiFi. Third, the
assessment of exposure in 5G deployments including mm-Wave frequencies is
another line of research. Fourth, we plan to perform extensive assessment by
running commonly used smartphone applications (social media, video streaming,
online conference, etc.). Fifth, the decrease of exposure observed in LOS
locations suggest that deploying a dense 5G network, in which most of
territory is in LOS w.r.t. the serving RBS, is the best solution to reduce the
exposure from the terminals. This goal could be alternatively achieved by
installing intelligent surfaces (active or passive), to improve the signal
coverage over the territory. The evaluation of exposure in such innovative
deployments is therefore a future activity.

## Acknowledgements

This work was supported by the PLAN-EMF Project (KAUST-CNIT) under Award
OSR-2020-CRG9-4377.

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|  Luca Chiaraviglio (M’09-SM’16) received the Ph.D. degree in
telecommunication and electronics engineering from the Polytechnic Unvisersity
of Turin, Italy. He is currently an Associate Professor with the University of
Rome “Tor Vergata”, Italy, teaching the courses of “Internet of Things” and
“Networking and Internet”. He has co-authored more than 160 articles published
in international journals, books, and conferences. His current research
interests include 6G networks, electromagnetic fields assessments of 5G
networks, and health risks assessment of 5G communications. He received the
Best Paper Award at the IEEE Vehicular Technology Conference (VTC Spring), in
2020 and 2016, and at the Conference on Innovation in Clouds, Internet and
Networks (ICIN), in 2018, all of them appearing as the first author. Some of
his papers are listed as the Best Readings on Green Communications by the
IEEE. Moreover, he has been recognized as an author in the top 1% most highly
cited papers in the Information and Communication Technology (ICT) field
worldwide and top 2% world scientists according to the 2021 and 2022 updates
of the science-wide author databases of standardized citation indicators.
---|---
|  Chiara Lodovisi received the Ph.D. degree in engineering electronics from
the University of Rome Tor Vergata, Italy. She worked for five years as a RF
Engineer Consultant for H3G mobile operator and then as a Research Associate
at the University of Rome Tor Vergata, focusing on optical communications,
submarine and satellite optical links, and radio over fiber. She is currently
a Researcher with CNIT, Italy. Her research interests include 5G networks,
health risk assessment of 5G communications, interoperability over fiber
between TETRA/LTE systems, and 5G networks.
---|---
|  Stefania Bartoletti received the Laurea degree (summa cum laude) in
electronics and telecommunications engineering and the Ph.D. degree in
information engineering from the University of Ferrara, Ferrara, Italy, in
2011 and 2015, respectively. She is currently Assistant Professor at the
University of Rome “Tor Vergata”, Italy. She was a Marie Sklodowska-Curie
Global Fellow within the Horizon 2020 European Framework for a research
project with the Wireless Information and Network Sciences Laboratory,
Massachusetts Institute of Technology (MIT), Cambridge, MA, USA, and the
University of Ferrara during 2016-2019. Her research interests include theory
and experimentation of wireless networks for passive localization and physical
behavior analysis. Dr. Bartoletti was the recipient of the 2016 Paul Baran
Young Scholar Award of the Marconi Society. She served as a Chair of the TPC
for the IEEE ICC and Globecom Workshops on Advances in Network Localization
and Navigation (ANLN) from 2017 to 2021, and as reviewer for numerous IEEE
journals and international conferences. She is Editor of the IEEE
Communications Letters.
---|---
|  Ahmed Elzanaty (Senior Member, IEEE) received the Ph.D. degree (cum laude)
in electronics, telecommunications, and information technology from the
University of Bologna, Italy, in 2018. He was a Research Fellow with the
University of Bologna from 2017 to 2019. He was a Post-Doctoral Fellow with
the King Abdullah University of Science and Technology, Saudi Arabia. He is
currently a Lecturer (Assistant Professor) with the Institute for
Communication Systems, University of Surrey, U.K. He has participated in
several national and European projects, such as GRETA and EuroCPS. His
research interests include cellular networks design with EMF constraints,
coded modulation, compressive sensing, and distributed training of neural
networks.
---|---
|  Mohamed Slim Alouini was born in Tunis, Tunisia. He received the Ph.D.
degree in Electrical Engineering from the California Institute of Technology
(Caltech) in 1998. He served as a faculty member at the University of
Minnesota then in the Texas A&M University at Qatar before joining in 2009 the
King Abdullah University of Science and Technology (KAUST) where he is now a
Distinguished Professor of Electrical and Computer Engineering. Prof. Alouini
is a Fellow of the IEEE and OPTICA (Formerly the Optical Society of America
(OSA)). He is currently particularly interested in addressing the technical
challenges associated with the uneven distribution, access to, and use of
information and communication technologies in rural, low-income, disaster,
and/or hard-to-reach areas.
---|---
  *[EMF]: Electro-Magnetic Field
  *[WHO]: World Health Organization
  *[ICNIRP]: International Commission on Non-Ionizing Radiation Protection
  *[IEEE]: Institute of Electrical and Electronics Engineers
  *[RBSs]: Radio Base Station
  *[RBS]: Radio Base Station
  *[UE]: User Equipment
  *[UL]: uplink
  *[DL]: downlink
  *[NLOS]: Non-Line-of-Sight
  *[LOS]: Line-of-Sight
  *[FWA]: Fixed Wireless Access
  *[NSA]: Non Stand Alone
  *[SA]: Stand Alone
  *[FDD]: Frequency Division Duplexing
  *[TDD]: Time Division Duplexing
  *[HW]: HardWare
  *[SAR]: Specific Absorption Rate
  *[SAN]: Spectrum ANalyzer
  *[IP]: Internet Protocol
  *[ADC]: Analog-to-Digital Converter
  *[SCPI]: Standard Commands for Programmable Instruments
  *[TCP]: Transmission Control Protocol
  *[RMS]: root mean square