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
ECDF

    Empirical Cumulative Distribution Function
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
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
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
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
NLOS

    non-line-of-sight
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-StandAlone
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
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
SA

    StandAlone
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
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

# What is the Impact of 5G Towers on the Exposure over Children, Teenagers and
Sensitive Buildings?

Luca Chiaraviglio,(1,2) Chiara Lodovisi,(1,2) Daniele Franci,(3) Enrico
Grillo,(3)
Settimio Pavoncello,(3) Tommaso Aureli,(3) Nicola Blefari-Melazzi,(1,2)
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, Parma,
Italy
(3) Agenzia Regionale per la Protezione Ambientale del Lazio (ARPA Lazio),
Rome, Italy,
{daniele.franci, enrico.grillo, settimio.pavoncello,
<EMAIL_ADDRESS>
(4) CEMSE Division, King Abdullah University of Science and Technology,
Thuwal, Saudi Arabia
email<EMAIL_ADDRESS>
###### Abstract

The rolling-out of 5G antennas over the territory is a fundamental step to
provide 5G connectivity. However, little efforts have been done so far on the
exposure assessment from 5G cellular towers over young people and sensitive
buildings like schools and medical centers. The goal of this article is
therefore to explain why the 5G service strongly leverages the deployment of
5G antennas over cellular towers, how a tower installation hosting 5G antennas
affects exposure over the territory, and at what extent children/teenagers,
schools and medical centers are impacted by the exposure from 5G towers.

5G networks are being deployed in many countries of the world. Despite the
potentials of this technology have not been completely unleashed, a central
point of debate is how 5G influences Electro-Magnetic Field (EMF) exposure
over the population, and whether such exposure patterns will substantially
increase in the forthcoming years [1]. In this context, the assessment of the
exposure levels generated by mobile networks over children and teenagers is
gaining attention in the academic community (see e.g., [2, 3]), as young
people will (probably) receive higher exposure levels throughout their lives
compared to older people - who tended to live in an era where mobile networks
were not so widespread as today. In addition, the exposure assessment over
sensitive places, such as schools and medical centers, is central for
monitoring aspects, e.g., to (possibly) limit the amount of unwanted exposure
over specific groups like students and patients.

Currently, there is no scientific evidence that base station exposure adhering
to international guidelines (such as the International Commission on Non-
Ionizing Radiation Protection (ICNIRP) ones [4]) is harmful for health [5, 6].
Nevertheless, the assessment of EMF radiated by base stations over children,
teenagers and sensitive places may be a key aspect for current (and future)
epidemiological studies [7], aimed at studying possible (but still not proven
at present time) correlations between the levels of 5G exposure and the
emergence of long-term health diseases (such as cancers and tumors). Despite
the literature about EMF exposure assessment from mobile base stations
(including other Radio-Frequency (RF) sources) is already rather mature (see
e.g., [8, 9, 10] for pre-5G studies and [11, 12, 13, 14, 15] for 5G ones),
there is currently a lack of studies focusing on the exposure radiated by 5G
outdoor base stations over children, teenagers, and sensitive places.

More concretely, Morelli et al. [16] evaluate the EMF levels over children and
adults by considering user terminals (and not base stations) as sources of
exposure. Bonato et al. [17] investigate the personal exposure from indoor
access points. Personal pre-5G exposure over children is collected through
exposimeters and thoroughly analyzed by Birks et al. [18]. Pre-5G exposure in
sensitive places (school buildings) is measured by Ramirez-Vazquez et al.
[19]. Gallastegi et al. [20] perform a thorough assessment of pre-5G exposure
over different areas (including homes and schools). Eventually, pre-5G
exposure in kinder-gardens is analyzed by Bhatt et al. [21]. Finally, 5G
exposure measurements for different locations in South Korea are analyzed by
Selmaoui et al. [15]. Despite we recognize the importance of such previous
works, none of them is tailored to the assessment of 5G exposure from outdoor
base stations over children, teenagers and sensitive areas - due to a
different goal (e.g., personal devices rather than base stations), and/or a
different focus (i.e., generic exposure assessment not tailored to
children/teenagers and sensitive buildings), and/or a lack of exposure
information about 5G (outdoor) base stations over the considered scenarios.

Figure 1: Roof-top installation in the city of Rome encompassing different
operators, sectors and antennas (including 5G ones).

Our work goes one step further with respect to the literature, by shedding
light on the exposure levels radiated by outdoor base stations (including 5G
ones) over young people and sensitive places. We tackle this interesting
problem by designing and evaluating a solid exposure assessment over realistic
scenarios and conservative settings. Our work links together children and
teenager data taken from national census, real positioning of the buildings
(including the sensitive ones), real positioning of 5G (and pre-5G) base
stations over the territory, realistic radio configurations and a conservative
computation of exposure from each base station - including equipment operating
on mm-Wave frequencies. In addition, we evaluate the exposure trends over the
coming years, by integrating in our analysis the impact of 5G deployment level
(i.e., from an early deployment up to maturity).

A key point of this article is the focus on the exposure levels radiated by 5G
towers, i.e., base stations installed on self poles - a.k.a. “raw-land” - and
roof-top installations, like the one shown in Fig. 1. We intentionally leave
apart from our analysis the radiation from small cells - a.k.a. micro
installations -, whose exposure levels would be additive with respect to the
ones radiated by 5G towers and considered in this work. Therefore, a natural
question is: Why do we concentrate on 5G towers? To answer this question, we
first present a brief overview on how 5G is currently implemented in
commercial networks and the role of towers on the 5G deployment. We then
analyse the root causes to explain why towers hosting 5G antennas are relevant
for the assessment of 5G exposure. Finally, we present the results of our
exposure analysis over children, teenagers and sensitive places. The work is
complemented by an Appendix providing more technical details - which may be
useful e.g., for the experts working in the field.

##  I How is 5G implemented in commercial networks?

The provisioning of the 5G service requires (obviously) the installation of
(new) antennas over the territory. Each antenna is placed on a physical
medium, which can be either a stand-alone pole for tower installations (roof-
top or raw-land), or alternatively a wall/lighting pole (particularly for
small cell deployments). In many countries in the world - such as Italy for
example - the tower installation is currently the preferred option for
deploying 5G networks, due to different reasons. First, cellular operators can
reuse the existing towers - currently hosting pre-5G equipment - to install 5G
antennas. In this way, the authorization steps for the installation of 5G
antennas may be simplified and/or shortened in time. In addition, this choice
tends to reduce the installation costs that are faced by operators compared to
the design of a new site from the scratch - since electricity and wired
connections required by 5G antennas are already available on the existing
site. Moreover, small cells are not pervasively installed over the entire
territory - being such option profitable only for those locations that provide
an adequate return on investment. In particular, small base stations are
instrumental to boost network capacity in shopping malls, conference centers,
airports, train stations and stadium - where the mobile traffic is intense due
to the simultaneous presence of thousands of terminals demanding wireless
services in a very limited area. On the contrary, the tower installation tends
to be the most profitable option to serve wide areas in rural zones and entire
portions of neighborhoods in urban deployments, with the ultimate goal of
providing ubiquitous wireless coverage over the territory.

Having understood that towers are a fundamental way to realize 5G networks, we
provide hereafter more technical details about the implementation of the 5G
service in practice. A key driver to speed-up the 5G network deployment is the
exploitation of the already installed 4G architecture. More concretely, 5G is
often realized through the Non-StandAlone (NSA) configuration option [22],
which allows running 5G network in conjunction (and not in parallel) with 4G.
In this way, the 4G network is used to spread control signals (that are
instrumental for example to select the base station serving a 5G user
terminal), as well as to run the inner functionalities on the 4G network core.
In the long term, 5G will be realized through the StandAlone (SA) option [22]
\- taking full control over wireless signals and network core functionalities.
To this aim, many countries and single operators in the world have announced
detailed plans for adopting 5G SA networks [23]. However, 4G networks will be
still in operation even when the majority of connections will be realized
through 5G - in order to guarantee legacy services. Therefore, the
contribution of 4G antennas on the level of exposure can not be neglected -
also to understand the exposure contribution of 5G with respect to pre-5G
sources.

##  II Why are 5G towers relevant for the assessment of the exposure?

A tower installation typically hosts multiple antenna panels. State-of-the-art
solutions for deploying 5G (and pre-5G) services include multiple-band antenna
(see e.g., [24]), supporting sub-GHz 5G frequencies, as well as most of
frequencies in use by pre-5G technologies (4G and even 2G). In addition,
dedicated antennas are used to deploy 5G over higher frequencies, such as mid-
band [25] and mm-Wave. The number of radiating sources in each antenna is not
marginal, as 5G panels operating on mid-band and mm-Wave are composed of
multiple radiating elements to realize Massive- multiple-input multiple-output
(MIMO) and beamforming functionalities. This number is then increased when the
tower hosts multiple physical operators - each of them managing a set of
antennas on the site. Therefore, the composite power radiated by all antennas
of the tower is not negligible, especially in its surroundings - where most of
radiation tends to concentrate.

In this context, the assessment of the EMF radiated by a cellular tower is a
fundamental (yet challenging) step. When considering urban zones, roof-top or
raw-land towers are commonly installed in proximity to residential buildings,
as well as sensitive ones. Clearly, the tower radiation level over its
surroundings strongly depends on multiple aspects, including: i) the
characteristics of the antennas installed on the tower (in terms, e.g., of
adopted frequency, maximum radiated power, maximum gain, radiation diagram,
horizontal orientation, mechanical/eletrical tilting), ii) the relative 3D
distance of the EMF evaluation point with respect to the considered tower and
iii) attenuation effects introduced e.g., by walls and obstacles between the
evaluation point and the tower.

The exposure assessment is further complicated when considering 5G antennas
operating in the mid-band and mm-Wave frequencies, as such equipment implement
smart antenna management features to realize the already mentioned massive-
MIMO and beamforming features - generally resulting in different exposure
levels compared to pre-5G equipment (see e.g., [12]). More in depth, the
output power of a 5G antenna is split among multiple elements of the antenna
panel, which are used to synthesize control and traffic beams over different
locations of the territory. As a consequence, assuming that the whole amount
of power radiated by the antenna panel is directed towards a single location
of the territory is not realistic in practice, and proper power scaling
factors have to be applied for a meaningful assessment of the EMF exposure
[26].

(a) Buildings and considered area - Spinaceto.

(b) Positioning of the sensitive places and cellular towers - Spinaceto.

(c) Buildings and considered area - Ponte-Parione.

(d) Positioning of the sensitive places and cellular towers - Ponte-Parione.

Figure 2: Towers, buildings and sensitive locations in the considered
scenarios (sub-figures best viewed in colors).

Interestingly, large scaling factors are applied to mid-band/mm-Wave antennas
covering areas dense of buildings (e.g., urban zones) compared to ones serving
sparse sets of houses (e.g., rural territory) [26, 27]. As a consequence, the
exposure from a single tower may be potentially lower in a dense area compared
to a sparse one. Intuitively, in fact, the beams in dense areas are more
uniformly distributed over the territory compared to sparse zones, in which
instead most of the beams are focused on specific locations (e.g., the few
locations with buildings), which therefore may receive an higher amount of
exposure with respect to the dense case. However, the composite exposure
depends also on the tower density, i.e., the number of towers simultaneously
covering the same area of territory. In a dense area, the tower density is
naturally higher compared to a sparse one (mainly due to higher capacity
requirements), thus potentially increasing the total exposure that is radiated
by multiple towers covering the same location [28].

(a) Roof-top sector with two operators.

(b) Raw-land sector with three operators.

Figure 3: Antenna panel installations for each operator (example with one
sector per operator): roof-top tower (left) vs. raw-land tower (right).

##  III Which is the impact of 5G towers on the exposure levels?

Up to this point, we have understood that exposure from 5G towers strongly
depends on the considered scenario. In the following, therefore, we present a
sound exposure assessment performed over two representative areas in the city
of Rome, by considering a dense neighborhood in the historical-center (called
Ponte-Parione) and a sparse one (called Spinaceto). We believe that such
scenarios are able to capture multiple aspects that strongly affect the
exposure levels, including different 5G power scaling factors (which depend,
e.g., on the sparse vs. dense building distribution), as well as different
tower densities. The whole description of the input data and the methodology
is detailed in the Appendix, while here we concetrate on the main outcomes.

The real positioning of buildings (including schools and medical centers) and
cellular towers is reported in Fig. 2. More in depth, we have included in our
analysis all the towers that are located inside the considered area, as well
as all the outer ones that are installed within 300 m from the area border -
in order to avoid a potential exposure underestimation on the borders. Several
considerations hold by analyzing the subfigures Fig. 2(a)-2(d). First, the
building density in Fig. 2(a),2(c) is clearly higher in Ponte-Parione compared
to Spinaceto: this is due to the fact that the former mainly includes
historical buildings closely placed to each other, while the latter is a newer
neighborhood, typically composed of spatially-distanced buildings - including
large green areas without any building. Second, both scenarios include
sensitive centers (schools and medical centers), which are spread over the
considered territory. Third, each school/medical center is normally composed
of different buildings (adjacent to each other), as shown by yellow and cyan
colors in Fig.2(a),2(c). Fourth, the towers are rather spread over Ponte-
Parione territory (Fig. 2(d)), while in Spinaceto the towers tend to be mainly
placed in proximity to the buildings (Fig. 2(b)). Fifth, the roof-top
installation is the preferred option for Spinaceto - which however hosts some
raw-land towers. On the contrary, Ponte-Parione does not include any raw-land,
mainly due to the fact that such tower type generally requires to find ground
locations without any buildings in the close surroundings - a hard goal to
pursue in Ponte-Parione. Sixth, many towers are located close to the sensitive
buildings (Fig. 2(b),2(d)). Seventh, the majority of installations in
Spinaceto host at least two distinct operators on the same tower, while the
option with a single operator per tower is the most used in Ponte-Parione.

Apart from the number of operators per tower, we have also collected sector
information for each installation (including the number of sectors and the
sector orientation). We have then designed two panel installations for each
sector and each type of tower in the scenario, as shown in Fig. 3 for a
representative example with one sector. More in depth, we have assumed that
each sector of each operator is composed of three distinct antenna panels: a
quadri-band panel (used for sub-GHz 5G and 2G/4G frequencies), a mid-band
panel (used for 5G frequencies operating in the 3.6-3.8 GHz range) and a mm-
Wave panel (used for 5G frequencies in the range 26.5-27.5 GHz). By adopting a
uniform panel design across all operators, we are able to make a fair
analysis, namely: i) all operators offer similar 5G connectivity options over
the scenarios, and ii) all frequencies currently allocated to 5G in Italy are
used. The features of each panel in terms of tilting/power/gain/radiation
diagram are taken from real equipment and realistic configurations, which in
turn depend on the tower type (details in the Appendix).

Eventually, Fig. 3(a) reports (in scale) the positioning of two operators for
the roof-top installation, while Fig. 3(b) highlights the positioning of three
operators (again in scale) for a raw-land installation. In both cases, the
antenna panels of different operators are vertically stacked (rather than
horizontally stacked). The adoption of a vertical alignment rather than an
horizontal one (in which all panels are placed on the pole summit) introduces
multiple advantages, namely: i) a conservative evaluation of exposure, since
the panels tend to be placed at a lower altitude (when multiple operators are
hosted), and ii) a uniform tower design across all installation sites, as each
tower is always composed of a single pole (while with an horizontal alignment
other supporting poles might be needed - depending on the number of operators
hosted by the tower).

(a) Spinaceto

(b) Ponte-Parione

Figure 4: Average EMF values of the building exposure vs. level of 5G adoption
(sub-figures best viewed in colors).

We then compute the EMF levels over all the buildings in the considered
scenarios, by following the methodology reported in the Appendix. Unless
otherwise specified, the EMF assessment is based on a set of conservative (and
worst-case) assumptions, which include: i) EMF evaluated on the building roof
level, ii) free-space propagation conditions, iii) no building attenuation,
iv) pre-5G antennas and sub-GHz 5G antennas radiating at the maximum power
(corresponding to the maximum traffic condition), v) mid-band and mm-Wave 5G
power scaling factors depending on the building distribution (sparse vs.
dense) and the level of 5G adoption (i.e., from early deployment to maturity).
The building exposure value is then assigned to the children and teenagers
living in that building (based on census information). In addition, the
exposure of schools and medical centers is extracted from the set of
buildings.

(a) Spinaceto

(b) Ponte-Parione

Figure 5: Impact of the 5G adoption level on the average exposure over
children and teenagers (sub-figures best viewed in colors).

(a) Children and teenagers - Spinaceto.

(b) Schools - Spinaceto

(c) Med. Buildings - Spinaceto

(d) Children and teenagers - Ponte-Parione.

(e) Schools - Ponte-Parione

(f) Med. Buildings - Ponte-Parione

Figure 6: Percentage of children and teenagers (left), schools (center) and
med. buildings (right) vs. EMF exposure.

We then move our attention on the analysis of the obtained results. Fig. 4
reports the average EMF over the buildings, by differentiating between the
building type and the level of 5G adoption. Intuitively, the power radiated by
mid-band/mm-Wave 5G antennas is proportional to the adoption level, and
therefore the exposure tends to (obviously) increase when passing from an
early level to the maturity one.

The figure then further differentiates between Spinaceto (Fig. 4(a)) and
Ponte-Parione (Fig. 4(b)). Several considerations hold by comparing Fig.
4(a)-4(b). First, the exposure over schools is rather similar to the one
received by the whole set of buildings in both scenarios. On the contrary,
medical buildings receive a lower exposure compared to the other building
categories. Second, the exposure tends to increase when passing from early to
maturity levels of 5G adoption - in all scenarios (as expected). However, the
maximum exposure increase is typically lower than 2 V/m and 3 V/m for
Spinaceto and Ponte-Parione, respectively. Fourth, the buildings in Ponte-
Parione receive an higher average exposure than the ones in Spinaceto, for all
5G adoption levels. This trend may be explained by the higher tower density in
Ponte-Parione compared to Spinaceto. Therefore, despite the 5G radiated power
per tower is lower in Ponte-Parione than in Spinaceto, the composite exposure
from all towers is higher in the former compared to the latter.

In the following step, we focus on the different exposure components, by
considering the average exposure that is received over children and teenagers
living in the two areas. To this aim, Fig. 5 differentiates between: i) pre-5G
exposure, ii) 5G exposure, iii) whole exposure (5G + pre-5G). The analysis is
then repeated for the different adoption levels of 5G (from early level to
maturity one). Interestingly, the contribution of 5G is lower than the pre-5G
one for the early 5G adoption case. This outcome corresponds to the current
situation, in which 5G represents a fraction of the exposure generated by
pre-5G antennas (mainly 4G and 2G) [13]. Then, when passing from early to
medium adoption level, the contribution of 5G exposure tends to increase - as
a consequence of the radiated power growth by mid-band and mm-Wave antennas.
At last, the exposure from 5G becomes higher that the pre-5G one. This event
occurs when a medium adoption level is achieved in Spinaceto (Fig. 5(a)) and
only when a maturity level is reached by Ponte-Parione (Fig. 5(b)).
Intuitively, in fact, the different power scaling factors applied in the two
scenarios (resulting from the different spatial distribution of the beams)
determine the share of 5G exposure with respect to the pre-5G one.

Up to this point, we have considered the average exposure level, without
analyzing the exposure variations of the single samples in each group
(children/teenagers, schools and medical buildings). To shed light on this
issue, Fig. 6 reports the EMF exposure vs. the percentage of
children/teenagers, school buildings and medical buildings over the two
scenarios. Each curve can be analyzed in this way: given a percentage value,
the corresponding EMF value read on the curve is the maximum exposure that is
achieved with that percentage. Clearly, when the percentage equals 100%, the
EMF value read on the curve is the maximum EMF exposure over all the samples.
The slope of the curve captures the EMF exposure variation over the samples:
when the curve is almost vertical, the variation is low; when the curve is
more horizontal, the variation is increased. Each curve in the subfigures then
highlights the following EMF metrics: i) pre-5G exposure (continuous line),
ii) 5G exposure - early adoption (dashed line), iii) 5G exposure - medium
adoption (dashed-dotted line), iv) 5G exposure - maturity adoption (dotted
line).

Several considerations hold by observing Fig. 6(a)-Fig. 6(f). First of all,
the curves capturing the exposure over children and teenagers are smoother
compared to the ones of schools and medical buildings. This is due to the fact
that children and teenagers are rather spread over the buildings in the
considered scenarios, while on the contrary schools and medical centers
represent a small fraction of the total buildings. As a result, the number of
samples is larger for the formers and smaller for the latters, and thus
resulting in smooth lines. Second, EMF exposure due to 5G (early adoption) is
lower than pre-5G one for all percentages of children/teenagers (Fig.
6(a),6(d)) and school buildings (Fig. 6(b),6(e)). In practical words, a
child/teenager or a school building always receives an higher amount of
exposure from pre-5G antennas than 5G ones during the early 5G adoption level.
Third, the evolution of 5G adoption towards maturity results in larger EMF
exposure levels by 5G compared to pre-5G ones for children/teenagers and
sensitive places, in both scenarios. Fifth, the maximum 5G exposure is lower
than 15 V/m for children and teenagers, while always lower than 10 V/m for
schools and medical buildings - well below maximum EMF exposure limits defined
by ICNIRP for the general public over the considered frequencies [4].111We
have also verified that the composite exposure of 5G and pre-5G adheres to the
EMF limits (details in the Appendix). Sixth, the exposure in Ponte-Parione
tends to be higher than Spinaceto for almost all the samples when considering
the medium and maturity 5G adoption cases, due again to the higher tower
density of the former with respect to the latter.

Figure 7: Impact of introducing the building attenuation on the EMF exposure
over children and teenagers (Ponte-Parione scenario - 5G maturity setting).

In the final part of our analysis, we investigate the impact of including
different attenuation levels on the EMF assessment over children and
teenagers. More in depth, we exploit the attenuation model defined in the
International Telecommunication Union (ITU) recommendation P.2109-1 [29] to
compute the attenuation values. Clearly, computing the exact attenuation
values for each child/teenager is a very challenging task. In particular, the
attenuation level depends on multiple building features (such as building
materials, walls thickness, windows/doors type and positioning, glass type,
etc.), as well as the positioning of children/teenagers inside the building
(e.g., proximity vs. farness with respect to the windows), which are
(obviously) not under our control. Therefore, rather than targeting the exact
computation of the attenuation for each child/teenager, we consider two
representative cases, namely: i) low attenuation level (computed with the ITU
model by setting 1% of probability) and ii) medium attenuation level (computed
by averaging the attenuation values retrieved from the ITU model between 1%
and 99% of probability).

Fig. 7 shows the EMF exposure over children and teenagers in Ponte-Parione
(with 5G maturity setting), by also including the case without attenuation for
comparison. As expected, the attenuation level has a large impact on the
received exposure over children and teenagers, resulting in a notable exposure
decrease for the medium setting compared to the other cases. In addition, the
attenuation strongly affects the relative share of 5G exposure with respect to
pre-5G one. Without attenuation, in fact, 5G exposure is higher than pre-5G
one. When attenuation is introduced, the share of 5G exposure with respect to
pre-5G one is reduced, and eventually the exposure from 5G becomes lower than
the pre-5G one when a medium attenuation is assumed. This effect is expected,
as 5G includes mid-band and and mm-Wave frequencies, which are higher than the
ones in use by pre-5G, and are therefore subject to larger propagation losses.

##  IV Summary and Future Work

We have assessed the impact of 5G towers on the EMF exposure over
children/teenagers, school buildings and medical buildings over two realistic
scenarios. Our results reveal that, although 5G exposure radiated by towers is
initially lower than the pre-5G one, 5G will become the dominant source of
exposure from cellular towers when a maturity level will be reached. However,
the scaling factors applied to the maximum power radiated by mid-band/mm-Wave
antennas, the tower distribution and the positioning of the buildings are
important aspects heavily influencing the exposure levels over young people
and sensitive places. Eventually, the actual level of exposure over children
and teenagers is largely impacted by the building attenuation level, which has
a stronger effect over mid-band and mm-Wave 5G frequecies compared to pre-5G
ones.

As future works, we plan to extend our assessment to whole
municipalities/cities, including zones covered by small cells. In addition, as
propagation has a strong effect over the exposure received by children and
teenagers, massive campaigns of EMF measurements from 5G towers should be
performed, especially inside the buildings. Finally, the joint assessment of
5G exposure from both towers and other installations - such as intelligent
reflecting surfaces and distributed antenna systems - is another interesting
step. This activity will also include the exposure trends analysis of pre-5G
sources.

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##  Appendix A Methodology and Additional Results

We provide hereafter more insights about the following aspects: i) collection
of buildings and population data, ii) collection of tower data, iii) exposure
assessment procedure, iv) description of the methodology to generate Fig. 4-7.
Finally, the Appendix is complemented by a set of additional results.

###  A-A Buildings and Population Data

Obtaining reliable information about buildings and population in the
considered areas is a fundamental step for the meaningfulness of the
considered analysis. Focusing on the buildings information (in terms of
positioning, volume, floors, shape), we have exploited the data available from
OpenStreetMap, a freely available database allowing exportation of the saved
data (see e.g., openstreetshape). In particular, the raw information retrieved
from OpenStreetMap has been processed by QGIS software, in order to filter the
subset of buildings falling within each area of interest (i.e., Spinaceto or
Ponte-Parione).

Focusing on the sensitive buildings, we have selected schools and medical
buildings based on online information publicly available from the
municipality. In the following step, we have used QGIS to filter the set of
sensitive buildings (as a ShapeFile) from the entire set of buildings in each
scenario.

Focusing then on population data, we have exploited the 2011 census done by
ISTAT - the Italian national statistics institute - and freely available from
istatvariables, in order to obtain the number of children/teenagers and adults
in each building of the scenario. Although such information is not apparently
up-to-date (as more than 10 years passed from the last census), we believe
that it is in any case reliable and able to capture the current population
conditions for the areas under investigation, due to the following reasons: i)
national population has not substantially increased in the last 10 years, ii)
both Spinaceto and Ponte-Parione have been pervasively inhabited much earlier
than 10 years ago, iii) the urbanization level (in terms of buildings and
their exploitation) has not substantially changed over the last 10 years in
the considered neighborhoods.

Fig. 8 reports the number of inhabitants retrieved from the national census
over the two scenarios, for different age bins. Two considerations hold by
analyzing the figure. First, the distribution of inhabitants over the bins is
rather similar over the two scenarios. Second, the number of inhabitants
belonging to children and teenagers bins is not marginal, i.e., always more
than 1500 for Spinaceto and more than 700 in Ponte-Parione for each bin.

(a) Spinaceto

(b) Ponte-Parione

Figure 8: Breakdown of the population age. The population bins belonging to children and teenagers category are highlighted. TABLE I: Buildings and population comparison over the considered scenarios. | Metric Value
---|---
Metric Name | Spinaceto | Ponte-Parione
Size of the area |  3.51 km2 |  1.62 km2
Number of buildings | 1068 | 755
Building density |  304/km2 |  466/km2
Number of schools | 16 | 6
Number of school buildings | 58 | 14
Number of medical centers | 4 | 4
Number of medical buildings | 30 | 34
Number of inhabitants | 29987 | 19265
Inhabitants density |  8543/km2 |  11892/km2
Number of children and teenagers | 6298 | 3278
Children and teenagers density |  1974/km2 |  2023/km2

However, we point out that the population data collected by ISTAT is referred
to census zones, i.e., small portions of territory - each of them composed of
multiple buildings. On the contrary, our approach requires as input the
population information for each building. To solve this issue, we have
initially loaded the census zones on QGIS software. In the following step, we
have assigned each building $b$ of the scenario to its corresponding census
zone $z$. Finally, we have computed the number of children/teenagers
$N^{\text{CHD-TN}}_{b}$ and the number of adults $N^{\text{AD}}_{b}$ in each
building as:

$N^{\text{CHD-TN}}_{b}=\frac{N^{\text{CHD-TN}}_{z}\cdot V_{b}}{V_{z}}$ (1)
$N^{\text{AD}}_{b}=\frac{N^{\text{AD}}_{z}\cdot V_{b}}{V_{z}}$ (2)

where $N^{\text{CHD-TN}}_{z}$ ($N^{\text{AD}}_{b}$) is the number of
children/teenagers (adults) of census zone $z$, $V_{b}$ is the volume of
building $b$, and $V_{z}$ is the volume of all buildings falling within census
zone $z$. The output of this step is then a building map (as a ShapeFile),
enriched with the population information.

Finally, Tab. I summarizes the main building and population information
collected in the two scenarios. More in depth, the area in Spinaceto is larger
than Ponte-Parione, being also characterized by an higher number of buildings.
However, the building density in Spinaceto is lower than the one in Ponte-
Parione. Focusing then on the sensitive buildings, Spinaceto hosts a larger
number of schools compared to Ponte-Parione. However, the number of medical
centers is comparable over the two scenarios. Clearly, each school and medical
center is composed of multiple buildings. Eventually, the number of
children/teenagers and inhabitants is higher in Spinaceto compared to Ponte-
Parione. However, the opposite holds when considering the population density.
Finally, the total number of children and teenagers is rather large, i.e.,
several thousands in each scenario.

###  A-B Tower Data

TABLE II: Tower Characterization. Feature ID | Feature | Description
---|---|---
F1 | Type | Roof-top or raw-land
F2 | Positioning | GPS coordinates and altitude
F3 | Operators | Number and IDs
|  | Number of sectors, sector orientation
F4 | Sectors | (per operator)
|  | Number of panels, panel setup
F5 | Radiating sources | (per sector)

The second step required as input to our analysis is the collection of data to
characterize the towers in the considered scenarios. As reported in Tab. II,
the tower characterization includes the following features: F1) tower type
(roof-top or raw-land), F2) tower positioning, F3) operators hosted in each
tower, F4) number of sectors for each operator and orientation of each sector,
F5) radio configuration for each sector (in terms e.g., of installed panels
and radio configuration of each panel).

Given the aforementioned huge and detailed amount of information, two natural
questions emerge: How to collect such information over the territory? Is such
information able to capture the evolution of the network in the coming years?
To tackle both questions in a comprehensive and meaningful way, in this work
we have adopted a hybrid approach, in which F1)-F4) are sensed from the real
deployments realized in the considered scenarios, while F5) is derived from
reasonable assumptions about pre-5G and 5G deployments and realistic radio
configurations for each installed antenna panel. However, the skeptic of our
work may object that we are not directly sensing F5) from the actual
deployment over the territory, and hence our results are not meaningful.
Before going into the details of the collected input data, let us first
analyze such concern and better motivate the meaningfulness of our hybrid
approach.

####  A-B1 Hybrid Approach: Motivation

Since we are currently in the early days of 5G deployment, not all the towers
in the scenario host 5G equipment - many of them provide only pre-5G
connectivity. However, it is expected that 5G technology will become
predominant in the next (few) years, meaning that the panels installed on the
towers will be progressively updated/replaced to dismiss legacy equipment
(e.g., 3G antennas timsdismission) in favor of 5G radiating elements. This
picture is further complicated in the Italian country, as the presently
enforced exposure limits are more stringent that the ones proposed by
international organizations (such as ICNIRP ICNIRPGuidelines:20appendix) and
promoted by the European Commission. According to previous works (see e.g.,
ituimpact,chiaraviglio2018planning2), strict exposure regulations negatively
impact co-location of antennas on the same tower (and even installation of new
towers on the same building), thus (possibly) introducing strong barriers on
the deployment of 5G networks. In countries adopting stricter-than-ICNIRP
regulations, in fact, urban sites tend to be almost saturated in terms of
available EMF space - and therefore it is not possible to install new
radiating sources (without violating the exposure limits). As a result, more
towers have to be installed over the territory compared to countries adopting
ICNIRP regulations ituimpact. This picture has changed in some European
countries, like Poland and Lithunia, which have updated their national
regulations from stricter-than-ICNIRP to ICNIRP-based regulations.

In this context, the application of ICNIRP or stricter-than-ICNIRP regulations
influences the input data required by our work, as intuitively, the set of
radiating elements, and their radiated power, strongly depends on the adopted
regulation. In this work, we have chosen to derive a deployment that satisfies
the limits for general public defined by ICNIRP ICNIRPGuidelines:20appendix,
due to the following reasons. First, we are able to generalize our results to
the (many) countries adopting the ICNIRP regulations. Second, we consider a
conservative setting, in which the exposure is (likely) higher that the one
currently experienced over the territory - resulting from stricter-than-ICNIRP
regulations.

Obviously, an exposure regulation has an impact also on the planning on the
network, in terms e.g., of tower positioning, sector orientation, radiated
power setting, etc. In our case, we solve this issue by adopting a hybrid
approach, in which we retrieve the actual tower positioning and sector
orientation, while we define configurations of the radiating sources that
satisfy ICNIRP regulations. We believe that this approach is representative of
a worst-case scenario (in terms of exposure), due to the following reasons: i)
higher tower density resulting from the stricter-than-ICNIRP planning compared
to the ICNIRP one ituimpact, ii) radiated power from each source satisfying
ICNIRP limits (thus higher than the stricter-than-ICNIRP case).

####  A-B2 Collection of tower information

(a) Placement of the network scanner in the car

(b) Tower view during war-driving

(c) Raw sector orientation retrieved by the network scanner (map source:
Google Maps)

(d) Aerial view of the towers identified by the network scanner (image source:
Google Maps)

Figure 9: War-driving step performed with the network scanner.

We then provide more details about how the information F1)-F4) has been sensed
from Spinaceto and Ponte-Parione. More in depth, we initially extract the
relevant data from the documents sent by operators to ARPA Lazio in order to
get authorization approval for the tower.222No sensitive information reported
in the authorization documents is disclosed in our work. The collected
information includes tower type (roof-top vs. raw-land), GPS positioning of
the tower, installed operators, number of sectors and sectors orientation for
each tower. In the following step, we perform a cross-check of the obtained
data through a war-driving based approach. A tower check over the territory is
in fact mandatory, due to the following reasons: i) the information reported
in the tower authorization documents may be outdated (as subsequent tower
updates performed after the initial authorization may not generate new
authorization requests - this include e.g., tower disposal), ii) authorization
documents are not available for all the towers (e.g., some towers in Ponte-
Parione are placed on buildings belonging to the Vatican state - and those
authorization requests are not processed by ARPA Lazio).

TABLE III: Radio configuration for a single sector $s_{1}$ of roof-top type. Antenna ID $a$ | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8
---|---|---|---|---|---|---|---|---
Frequency Band $F_{a}$ | 700 MHz | 800 MHz | 900 MHz | 1800 MHz | 2100 MHz | 2600 MHz | 3700 MHz | 26000 MHz
Technology | 5G | 4G | 2G | 4G | 4G | 4G | 5G | 5G
Antenna Panel $p$ | QuadriBand | 5G-midBand | 5G-mmWave
Mechanical Tilt $\sigma_{(s_{1},p)}$ | 0° | 2°
Electrical Tilt $t$ | 2° | 4° | 2° | 4° | 2° | 0°
Output Power $P^{\text{MAX}}_{(s_{1},a)}$ | 40 W | 60 W | 80 W | 5 W
Maximum Gain $G^{\text{MAX}}_{(s_{2},a)}$ | 13.8 dBi | 14.4 dBi | 14.9 dBi | 16.4 dBi | 17.5 dBi | 17.7 dBi | 23.9 dBi | 33.5 dBi
| MSI file | MSI file | MSI file | MSI file | MSI file | MSI file | MSI file | MSI file
Horiz. Diagram $D^{\text{H}}_{(s_{1},a)}$ | $(F_{a_{1}},t_{1})$ | $(F_{a_{2}},t_{2})$ | $(F_{a_{3}},t_{3})$ | $(F_{a_{4}},t_{4})$ | $(F_{a_{5}},t_{5})$ | $(F_{a_{6}},t_{6})$ | $(F_{a_{7}},t_{7})$ | $(F_{a_{8}},t_{8})$
| MSI file | MSI file | MSI file | MSI file | MSI file | MSI file | MSI file | MSI file
Vert. Diagram $D^{\text{V}}_{(s_{1},a)}$ | $(F_{a_{1}},t_{1})$ | $(F_{a_{2}},t_{2})$ | $(F_{a_{3}},t_{3})$ | $(F_{a_{4}},t_{4})$ | $(F_{a_{5}},t_{5})$ | $(F_{a_{6}},t_{6})$ | $(F_{a_{7}},t_{7})$ | $(F_{a_{8}},t_{8})$
Reduction Factor $R_{p}$ | 1 | $\alpha^{\text{5G}}_{p}$
TABLE IV: Radio configuration for a single sector $s_{2}$ of raw-land type. Antenna ID $a$ | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8
---|---|---|---|---|---|---|---|---
Frequency Band $F_{a}$ | 700 MHz | 800 MHz | 900 MHz | 1800 MHz | 2100 MHz | 2600 MHz | 3700 MHz | 26000 MHz
Technology | 5G | 4G | 2G | 4G | 4G | 4G | 5G | 5G
Antenna Panel $p$ | QuadriBand | 5G-midBand | 5G-mmWave
Mechanical Tilt $\sigma_{(s_{2},p)}$ | 0° | 2°
Electrical Tilt | 4° | 6° | 4° | 6° | 2° | 0°
Output Power $P^{\text{MAX}}_{(s_{2},a)}$ | 60 W | 80 W | 100 W | 5 W
Maximum Gain $G^{\text{MAX}}_{(s_{2},a)}$ | 13.8 dBi | 14.4 dBi | 14.9 dBi | 16.4 dBi | 17.5 dBi | 17.7 dBi | 23.9 dBi | 33.5 dBi
| MSI file | MSI file | MSI file | MSI file | MSI file | MSI file | MSI file | MSI file
Horiz. Diagram $D^{\text{H}}_{(s_{2},a)}$ | $(F_{a_{1}},t_{1})$ | $(F_{a_{2}},t_{2})$ | $(F_{a_{3}},t_{3})$ | $(F_{a_{4}},t_{4})$ | $(F_{a_{5}},t_{5})$ | $(F_{a_{6}},t_{6})$ | $(F_{a_{7}},t_{7})$ | $(F_{a_{8}},t_{8})$
| MSI file | MSI file | MSI file | MSI file | MSI file | MSI file | MSI file | MSI file
Vert. Diagram $D^{\text{V}}_{(s_{2},a)}$ | $(F_{a_{1}},t_{1})$ | $(F_{a_{2}},t_{2})$ | $(F_{a_{3}},t_{3})$ | $(F_{a_{4}},t_{4})$ | $(F_{a_{5}},t_{5})$ | $(F_{a_{6}},t_{6})$ | $(F_{a_{7}},t_{7})$ | $(F_{a_{8}},t_{8})$
Reduction Factor $R_{p}$ | 1 | $\alpha^{\text{5G}}_{p}$
TABLE V: Statistical reduction factors for the different levels of 5G adoption. 5G Adoption Level |  $\alpha^{\text{5G}}_{p}$ values
---|---
Name | Index $l$ | Ponte-Parione | Spinaceto
Early | 1 | 0.03 | 0.05
Medium | 2 | 0.13 | 0.21
Maturity | 3 | 0.18 | 0.31

More concretely, the tower check is done in the following way. We install a
Rohde&Schwarz TSMA6 network scanner on a car, as shown Fig. 9(a). The network
scanner is then connected to a PCTEL P286H external omnidirectional antenna
(mounted on the roof on the car). In addition, electricity to the network
scanner is provided through a plug on the car. The scanner is then
complemented by a tablet, wirelessy connected to the scanner, and running the
Rohde&Schwarz ROMES4 commercial software for data acquisition and analysis. We
then extensively cover the streets in the considered scenarios, focusing in
particular on the zones where we identified the towers (Fig. 9(b)). Based on
the triangularization of the acquired signals, the software is able to
position the identified towers on a map, showing also the sector orientation
(Fig. 9(c)). This last information is instrumental for towers without
available authorization data, particularly those ones for which sector
orientation is hidden to sight (like the fake chimney in Fig. 9(b)). Clearly,
the raw information presented by the software (Fig. 9(c)) has to be manually
filtered, in order to remove duplicates and/or not precise tower positioning.
Eventually, Fig. 9(d) reports the towers that were completely characterized
through the war-driving approach (i.e., those ones with missing authorization
data). For all the other towers (i.e., the ones with available authorization),
the war-driving approach allowed identifying the active ones and discarding
the disposed ones. In addition, sector information was compared against the
one reported in the authorization documents, finding in general a good
matching between both sources.

TABLE VI: Example of a tower hosting two operators and three sectors per operator. |  |  | Electrical center height of panel $p$ (above sea level)
---|---|---|---
Operator | Number of sectors | Sectors Orientation $\rho^{\text{CLOCK}}_{s}$ | Quadriband | 5G-MidBand | 5G-mmWave
A | 3 | {80°, 200°, 280°} | 78.3 m | 78.8 m | 77.4 m
B | 3 | {70°, 220°, 330°} | 76 m | 76.5 m | 75.1 m

####  A-B3 Radio Configuration

The last phase of the tower characterization is the configuration of the
radiating sources for each sector. This step includes the setting of several
parameters, ranging from the selection of the adopted panels to the
configuration of the radio features for each panel. Without loss of
generality, in this work we adopt an uniform radio configuration for all the
sectors and all the operators, as shown in Tab. III and Tab. IV for the roof-
top and raw-land cases. More in depth, we adopt real parameters and realistic
settings, by assuming the deployment of three distinct antenna panels for each
sector (and each operator), namely: a quadri-band antenna panel, a mid-band
panel and mm-Wave one. The positioning (in scale) of the aforementioned panels
in roof-top and raw-land cases is shown in Fig. 3.

Clearly, the main application for mm-Wave panels is to boost capacity
(especially in small cell deployments). However, we believe that including a
mm-Wave panel on a tower is meaningful in our scenario, due to the following
reasons: i) authorization requests to install mm-Wave antennas over towers
have been already received (thus demonstrating that the operators are keen to
invest on towers for the installation of mm-Wave panels), ii) towers are
normally located close to densely populated areas, including main streets -
where 5G over mm-Wave may be instrumental to provide high-level and innovative
services.

TABLE VII: Tower metrics over the considered scenarios. | Metric Value
---|---
Metric Name | Ponte-Parione | Spinaceto
Number of operators | 4
Number of towers | 45 | 13
Number of roof-top towers | 45 | 10
Number of raw-land towers | 0 | 3
Number of pre-5G sources | 934 | 354
Number of 5G sources | 560 | 212

We then provide more details about the parameters reported in Tab. III-Tab.
IV. Naturally, the quadri-band panel includes sub-GHz band of 5G, as well as
the bands for 2G and 4G. In addition, the values for the mechanical/electrical
tilting and the output power are taken from typical settings adopted by
operators. Eventually, the output power of the mm-Wave panel is set to the
maximum one allowed by the equipment under consideration. The antenna gains
reported in the tables are retrieved from the datasheets of the panels.
Moreover, the radiation diagrams for each source are taken from the real ones
made available by the antenna vendors. Such information is included in files
with MSI format (i.e., a list of gain values for each angle in horizontal and
vertical planes). Each radiation diagram depends on the following features: i)
the panel model (highlighted with different colors in the tables), ii) the
operating frequency, and iii) the electrical tilting value. Finally, the last
row of Tab. III-IV reports the reduction factor that is applied to the maximum
power. In this work, we assume that the quadri-band panel always radiates at
the maximum power (corresponding to the values of output power reported in the
tables). On the other hand, the output power of mid-band and mm-Wave panels
$p$ is scaled by the factor $\alpha^{\text{5G}}_{p}$, which is set in
accordance to the realistic values provided by relevant standards
iec62669appendix.

(a) Translation of Global Coordinate System

(b) Rotation to Local Coordinate System

Figure 10: Change from global coordinate system to the local coordinate system
of a given panel $p$.

More in depth, $\alpha^{\text{5G}}_{p}$ captures the statistical reduction
factors that are introduced by the smart antenna management features of 5G
over mid-band and mm-Wave. More concretely, Tab. V reports the reduction
factors over the two scenarios and the different level of 5G adoptions. The
numerical values are taken from iec62669appendix, by assuming that Spinaceto
is representative for a sparse area while Ponte-Parione for a dense one. In
addition, the early/medium/maturity levels of 5G adoption correspond to
5%/50%/95% utilization level of iec62669appendix, respectively. By observing
the values in Tab. V, two considerations emerge. First, strong reduction
factors are applied even in the maturity case. Second, the reduction factors
are lower (i.e., stronger) in Spinaceto than in Ponte-Parione, due to the
lower and less uniform building density that characterizes the former with
respect to the latter.

For the sake of clarity, Tab. VI reports the sector information for a roof-top
tower in Spinaceto. In this case, the tower hosts two operators, each of them
managing three sectors. For each sector, the radio configuration in Tab. III
is implemented. Tab. VI details also the altitude of the electrical center for
teach panel type. The altitude of the each panel is retrieved in this way: i)
the roof-altitude is retrieved from OpenStreetMap database, ii) based on the
number of operators and number of sectors, a roof-top installation is defined
(based on e.g., Fig. 3), iii) the altitude in i) is summed to the height of
the each panel in the installation.

Eventually, Tab. VII reports some relevant tower metrics over Spinaceto and
Ponte-Parione. More in depth, all four national operators manage towers in
both scenarios. However, the number of towers in Ponte-Parione is larger than
in Spinaceto. Focusing on the tower type, Ponte-Parione includes only roof-top
installations, while Spinaceto hosts also some raw-lands. Obviously, the total
number of both pre-5G and pre-5G sources is clearly higher in Ponte-Parione
than in Spinaceto. However, we point out that the number of pre-5G sources is
always higher than the 5G ones, due to the larger number of frequency bands
managed by the formers (800, 900, 1800, 2100, 2600 MHz) with respect to the
latters (700, 3600, 26000 MHz).

###  A-C Exposure Assessment Procedure

In this part, we provide more details about the procedure to perform the
exposure assessment, given as input the tower and building information. We
initially focus on the exposure assessment for a generic pixel $c$ over the
territory, by considering the EMF that is radiated over $c$ by antenna $a$ of
panel $p$ installed at sector $s$. The considered methodology is fully
compliant with the national exposure assessement procedure currently adopted
in Italy ceiv1,ceiv2,ceiv3, which is in turn based on relevant international
exposure assessment standards iecexclusion.

More in depth, the sector orientation $\rho^{\text{CLOCK}}_{s}$ is normally
provided in counter-wise degrees, starting from the North axis. In our
assessment, however, the sector orientation $\rho_{s}$ is defined in counter-
wise degrees from East axis. Consequently, $\rho_{s}$ is expressed as:

$\rho_{s}=\begin{cases}\frac{\pi}{2}-\rho^{\text{CLOCK}}_{s}\quad\text{if}\quad
0\leq\rho^{\text{CLOCK}}_{s}\leq\pi/2\\\
\frac{5}{2}\pi-\rho^{\text{CLOCK}}_{s}\quad\text{if}\quad\pi/2<\rho^{\text{CLOCK}}_{s}\leq
2\pi\end{cases}$ (3)

The following step is the change of the global coordinate system to the local
one of the considered panel. This step is fundamental in order to properly
compute the relative orientation of the pixel with respect to the antenna
panel, and consequently for a thorough assessment of exposure. Fig. 10
sketches the steps that have to be applied in order to adopt the local
coordinate system of the panel. More concretely, a translation is applied
first (Fig. 10(a)), in order to move the coordinate system center on the
electrical center of the panel. In the following step, a rotation is performed
(Fig. 10(b)), in order to align the coordinate system to the relative
orientation in space of the panel.

(a) First Rotation

(b) Second Rotation

Figure 11: Graphical sketch of the rotations applied in $\Gamma_{(s,p)}$

More formally, let us denote with $[x_{(s,p)},y_{(s,p)},z_{(s,p)}]$ the 3D
global coordinates of the antenna panel $p$ of sector $s$. In a similar way,
let us denote with $[x_{c},y_{c},z_{c}]$ the global coordinates of pixel $c$.
Our goal is then to apply the change of coordinate system shown in Fig. 10 in
order to obtain the local coordinates
$[\tilde{x}_{(c,s,p)},\tilde{y}_{(c,s,p)},\tilde{z}_{(c,s,p)}$ of $c$ with
respect to panel $p$ in $s$. To this aim, we apply first the translation to
the local system and then the rotation with the following equation:

$\displaystyle\underbrace{\begin{bmatrix}\tilde{x}_{(c,s,p)}\\\
\tilde{y}_{(c,s,p)}\\\ \tilde{z}_{(c,s,p)}\end{bmatrix}}_{\text{Local
coordinates}}=\underbrace{\Gamma_{(s,p)}}_{\text{Rotation}}\cdot\underbrace{\begin{bmatrix}x_{c}-x_{(s,p)}\\\
y_{c}-y_{(s,p)}\\\ z_{c}-z_{(s,p)}\end{bmatrix}}_{\text{Translation}}$ (4)

where $\Gamma_{(s,p)}$ is the rotation matrix, formally expressed as:

$\small\Gamma_{(s,p)}=\begin{bmatrix}\cos{\rho_{s}}\cos{\sigma_{(s,p)}}&\sin{\rho_{s}}\cos{\sigma_{(s,p)}}&-\sin{\sigma_{(s,p)}}\\\
-\sin{\rho_{s}}&\cos{\rho_{s}}&0\\\
\cos{\rho_{s}}\sin{\sigma_{(s,p)}}&\sin{\rho_{s}}\sin{\sigma_{(s,p)}}&\cos{\sigma_{(s,p)}}\end{bmatrix}$
(5)

where we recall that $\rho_{s}$ is the orientation of sector $s$, while
$\sigma_{(s,p)}$ is the mechanical tilting of panel $p$ from sector $s$. A
graphical sketch of the rotations in $\Gamma_{(s,p)}$ is shown in Fig. 11.
Intuitively, the first rotation aligns the x axis of the local system to the
sector orientation, while the second one matches the z axis with the actual
mechanical tilting of the panel.

(a) $\theta_{(c,s,p)}$ Computation

(b) $\phi_{(c,s,p)}$ Computation

Figure 12: Graphical sketch for the computations of $\theta_{(c,s,p)}$ and
$\phi_{(c,s,p)}$.

In the following step, the 3D distance of the pixel $c$ with respect to the
panel $p$ in $s$ is computed:

$\small
r_{(c,s,p)}=\sqrt{\left(x_{c}-x_{(s,p)}\right)^{2}+\left(y_{c}-y_{(s,p)}\right)^{2}+\left(z_{c}-z_{(s,p)}\right)^{2}}$
(6)

We then concentrate on the assessement of the orientation of $c$ with respect
to $p$ in $s$. To this aim, we introduce horizontal plane and vertical plane
angles, formally expressed as $\theta_{(c,s,p)}$ and $\phi_{(c,s,p)}$. A
graphical sketch, useful to understand how such angles are derived, is shown
in Fig. 12. $\theta_{(c,s,p)}$ and $\phi_{(c,s,p)}$ are then formally
expressed as:

$\theta_{(c,s,p)}=\text{asin}\left(\frac{\tilde{z}_{(c,s,p)}}{r_{(c,s,p)}}\right)$
(7) $\displaystyle\phi_{(c,s,p)}$ $\displaystyle=$
$\displaystyle\text{asin}\left(\frac{\tilde{y}_{(c,s,p)}}{\sqrt{r^{2}_{(c,s,p)}-\tilde{z}^{2}_{(c,s,p)}}}\right)\text{sgn}(\tilde{x}_{(c,s,p)})+$
(8)
$\displaystyle+\frac{\pi}{2}\text{sgn}(\tilde{y}_{(c,s,p)})(1-\text{sgn}(\tilde{x}_{(c,s,p)}))$

At this point, we are able to compute the gain $G_{(c,s,a)}$ from antenna $a$
of panel $p$ installed over sector $s$ that is received over $c$, which is
expressed as in ceiv1,ceiv2:

$G_{(c,s,a)}=G^{\text{MAX}}_{(s,a)}\cdot
D^{\text{V}}_{(s,a)}(\theta_{(c,s,p)})\cdot
D^{\text{H}}_{(s,a)}(\phi_{(c,s,p)})$ (9)

where $G^{\text{MAX}}_{(s,a)}$ is the gain of antenna element $a$ of sector
$s$ and $D^{\text{V}}_{(s,a)}$ and $D^{\text{H}}_{(s,a)}$ are the numerical
gain values on the horizontal and vertical planes (respectively). In our
scenarios, we assume that $D^{\text{V}}_{(s,a)}$ and $D^{\text{H}}_{(s,a)}$
are provided in a linear scale in terms of scaling terms (lower than unity).
We recall that $D^{\text{V}}_{(s,a)}$ and $D^{\text{H}}_{(s,a)}$ are extracted
from the MSI files of each antenna $a$ (which in turn depend also on the
adopted frequency and the electrical tilting as shown in Tab. III-IV).

We then apply the model of ceiv1,ceiv2,ceiv3 (which is almost equivalent to
the point-source model of ITU itutk70), to evaluate the EMF over pixel $c$,
radiated by antenna $a$ from sector $s$. More formally, the exposure in terms
of power density $S_{(c,s,a)}$ is expressed as:

$S_{(c,s,a)}=\frac{P^{\text{MAX}}_{(s,a)}\cdot G_{(c,s,a)}\cdot
R_{p}}{4\pi\cdot r^{2}_{(c,s,a)}\cdot A_{(c,s,a)}}\cdot$ (10)

where $P^{\text{MAX}}_{(s,a)}$ is the maximum output power, $R_{p}$ is the
power reduction factor, $A_{(c,s,a)}$ is the attenuation over pixel $c$.

The total power density $S^{\text{TOT}}_{c}$ from all antennas is then
expressed as:

$S^{\text{TOT}}_{c}=\sum_{s}\sum_{a}S_{(c,s,a)}$ (11)

The total power density from 5G antennas is denoted as:

$S^{\text{5G}}_{c}=\sum_{s}\sum_{a}S_{(c,s,a)},\forall
a:F_{a}\in\mathcal{F}^{\text{5G}}$ (12)

where $\mathcal{F}^{\text{5G}}=\\{700,3700,26000~{}\text{MHz}\\}$.

Similarly, the total power density from pre-5G antennas is expressed as:

$S^{\text{PRE-5G}}_{c}=\sum_{s}\sum_{a}S_{(c,s,a)},\forall
a:F_{a}\in\mathcal{F}^{\text{PRE-5G}}$ (13)

where $\mathcal{F}^{\text{PRE-5G}}=\\{800$, $900$, $1800$, $2100$,
$2600~{}\text{MHz}\\}$.

Finally, the electric field $E^{(.)}_{c}$ of each of the previous terms is
computed out from the power density $S^{(.)}_{c}$ by assuming far-field
conditions:

$E^{(\cdot)}_{c}=\sqrt{S^{(\cdot)}_{c}\cdot 377}$ (14)

Up to this point, we have shed light on the exposure evaluation for a generic
pixel $c$. Let us then provide more details on the computation of the exposure
for each building $b$. To this aim, we generate a grid of pixels in the
horizontal plane with a tight resolution (i.e., $2\times 2$ m). We then
exploit QGIS to filter the subset of pixels in each building. In addition,
each pixel is enriched with the roof altitude of the building taken from the
map of the scenario. In the following step, we apply Eq. (3)-(14) to compute
the electric field for each pixel $c$. Finally, we compute the average
building exposure $E^{(\cdot)}_{b}$ as the root mean square exposure from all
the pixels belonging to $b$.

###  A-D Generation of the figures

In this part, we provide mode details about the generation of Fig. 4-7.

Average EMF over the buildings - Fig. 4. Step 1: computation of
$E^{\text{TOT}}_{c}$ for each adoption level (based on 5G reduction factors of
Tab. V); Step 2: computation of building exposure $E^{\text{TOT}}_{b}$, Step
3: computation of the average exposure over: all buildings, schools, medical
centers.

Average EMF over children and teenagers - Fig. 5. Step 1: computation of
$E^{\text{TOT}}_{c}$, $E^{\text{PRE-5G}}_{c}$, $E^{\text{5G}}_{c}$ for each
adoption level (based on 5G reduction factors of Tab. V); Step 2: computation
of building exposure $E^{\text{TOT}}_{b}$, $E^{\text{PRE-5G}}_{b}$,
$E^{\text{5G}}_{b}$; Step 3: computation of the average exposure over children
and teenagers from the building exposure in Step 2, weighed by the number of
children and teenagers in the building $N_{b}^{\text{CHD-TN}}$.

Percentage of children/teenagers, school buildings and medical buildings vs.
EMF exposure - Fig. 6. Step 1: computation of $E^{\text{TOT}}_{c}$,
$E^{\text{PRE-5G}}_{c}$, $E^{\text{5G}}_{c}$ for each adoption level (based on
5G reduction factors of Tab. V); Step 2: computation of building exposure
$E^{\text{TOT}}_{b}$, $E^{\text{PRE-5G}}_{b}$, $E^{\text{5G}}_{b}$; Step 3:
computation of the exposure over children and teenagers from the building
exposure in Step 2, weighed by the number of children and teenagers in the
building $N_{b}^{\text{CHD-TN}}$; Step 4: computation of the Empirical
Cumulative Distribution Function (ECDF) for each category (children/teenagers,
school buildings, medical buildings), each type of source (pre-5G, 5G) and
each level of 5G adoption (early, medium, maturity).

Impact of introducing the building attenuation - Fig. 7. Step 1: computation
of $E^{\text{TOT}}_{c}$, $E^{\text{PRE-5G}}_{c}$, $E^{\text{5G}}_{c}$ for each
adoption level (based on 5G reduction factors of Tab. V) and each level of
attenuation; Step 2: computation of building exposure $E^{\text{TOT}}_{b}$,
$E^{\text{PRE-5G}}_{b}$, $E^{\text{5G}}_{b}$; Step 3: computation of the
average exposure over children and teenagers from the building exposure in
Step 2, weighed by the number of children and teenagers in the building
$N_{b}^{\text{CHD-TN}}$.

###  A-E Additional Results

(a) Spinaceto

(b) Ponte-Parione

Figure 13: EMF exposure over the buildings (5G maturity setting).

In the last part of this Appendix, we provide a set of additional outcomes,
useful to complement Fig. 4-7. More in depth, Fig. 13 visually highlights the
total exposure over buildings in Spinaceto and Ponte-Parione, by imposing a
maturity level for 5G. Interestingly, the exposure over buildings strongly
varies across the two scenarios. Spinaceto is characterized by a relatively
low amount of exposure, being the highest value (colored in dark blue color)
limited to the few buildings close to the tower installations. On the other
hand, the relatively higher tower density in Ponte-Parione compared to
Spinaceto also results in higher exposure buildings (as expected). Clearly,
the zones in Ponte-Parione that are characterized by a low tower density (like
the one show in bottom-left part of Fig. 13(b)) are also the ones in which the
buildings receive a small amount of exposure.

(a) Percentage of people vs. EMF exposure - Spinaceto.

(b) Percentage of buildings vs. EMF exposure - Spinaceto.

(c) Percentage of people vs. EMF exposure - Ponte-Parione.

(d) Percentage of buildings vs. EMF exposure - Ponte-Parione.

Figure 14: Exposure levels for children/teenagers against adults and entire
population (left). Comparison of exposure levels vs. the type of the building
(right). Figures retrieved with a 5G maturity level.

Fig. 14 details the exposure levels that are received by the following
categories: i) children/teenagers vs. adults vs. entire population (Fig.
14(a)-14(c)), ii) schools vs. medical buildings vs. all buildings (Fig.
14(b)-14(d)). Three considerations hold by analyzing Fig. 14. First, all the
categories of people receive similar exposure levels. This outcome derives
from the distributions of children/teenagers and adults over the territory,
which are rather similar in the scenarios under considerations. Second,
schools and medical buildings tend to receive different exposure levels
compared to the category including all the buildings. Naturally, this is due
to the fact that schools and medical buildings represent a small fraction of
the total buildings, and therefore the positioning of each sensitive building,
as well as the relative positioning of the towers in its surroundings, play a
key role in determining the actual exposure levels. Third, the composite
exposure is overall lower than the ICNIRP limits for the general public over
the considered frequency range (between 36 V/m and 61 V/m), thus guaranteeing
adherence to international exposure guidelines.

(a) Schools - Spinaceto

(b) Med. Buildings - Spinaceto

(c) Schools - Ponte-Parione

(d) Med. Buildings - Ponte-Parione

Figure 15: Impact of the 5G adoption level on the average exposure over
schools (left) and med. buildings (right).

Finally, Fig. 15 reports the average EMF over schools and medical buildings,
by differentiating between: i) type of exposure sources (all, only 5G, only
pre-5G), 5G adoption level (early, medium, maturity) and considered scenario
(Spinaceto and Ponte-Parione). Overall, the exposure is proportional to the
adoption level (as expected). In addition, the exposure in Ponte-Parione is
higher than the one in Spinaceto (thus confirming the outcomes of Fig. 5).
Finally, the contribution of 5G exposure is higher than the pre-5G when
considering a 5G maturity level (again in line with Fig. 5).

ieeetr

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  * [12] ITU-T K.70 Mitigation techniques to limit human exposure to EMFs in the vicinity of radiocommunication stations.  Available at https://www.itu.int/rec/T-REC-K.70-201801-I/en, last accessed on 26th Feb. 2020.

  *[EMF]: Electro-Magnetic Field
  *[ICNIRP]: International Commission on Non-Ionizing Radiation Protection
  *[RF]: Radio-Frequency
  *[NSA]: Non-StandAlone
  *[SA]: StandAlone
  *[MIMO]: multiple-input multiple-output
  *[ITU]: International Telecommunication Union
  *[ECDF]: Empirical Cumulative Distribution Function