Published at : 03 Nov 2022
Volume : IJtech
Vol 13, No 6 (2022)
DOI : https://doi.org/10.14716/ijtech.v13i6.5928
Mohammed Ahmed Salem | Faculty of Engineering and Technology, Multimedia University, 75450, Bukit Beruang, Malaysia |
Heng Siong Lim | Faculty of Engineering and Technology, Multimedia University, 75450, Bukit Beruang, Malaysia |
Ming Yam Chua | School of Electrical Engineering and Artificial Intelligence, Xiamen University Malaysia, 43900, Sepang, Malaysia |
Su Fong Chien | MIMOS Berhad, 57000, Kuala Lumpur, Malaysia |
Charilaos C. Zarakovitis | ICT Department, AxonLogic IKE, 142 31, Athens, Greece |
Chiew Yean Ng | Department of Radiology, Columbia Asia Hospital, 47100, Puchong, Malaysia |
Noor Ziela Abd Rahman | Faculty of Engineering and Technology, Multimedia University, 75450, Bukit Beruang, Malaysia |
To provide enhanced mobile services, the 5G system is
expected to further densify its network infrastructure and scale up the
deployment of massive antenna arrays that emit high-energy beams using the
millimeter wave spectrum. These radically new features will significantly
impact the EMF exposure level in the 5G networks. In this paper, EMF exposure
for 5G mobile networks in a dense urban environment is investigated using a
raytracing approach for the uplink (UL) and downlink (DL). A massive multi-input multi-output antenna
with multiuser beamforming capability is considered for the 5G base station.
For DL, the maximum rate transmission (MRT) technique is used to direct the
beams toward all the active users, and total power density (PD) is used to
evaluate the EMF exposure level. On the other hand, EMF exposure due to UL is
investigated using electric field strength and specific absorption rate (SAR).
The proposed ray-tracing based EMF evaluation framework exploits detailed
information of the scenarios, including 3D building geometry, EM
characteristics, multipath propagation, user locations and beamforming
radiation pattern, to effectively evaluate the EMF’s spatial variation levels.
Following this evaluation procedure, the impact of different user densities and
distributions is analyzed in terms of PD and SAR. Results show that for DL, the
peak PD increases from 6.65 to 24.92 dBm/m2 when the number of
active users in the area increases from a single user to 100%. Considering the
worst-case scenario, the PD exposure reaches 62% of the ICNIRP’s limit.
Saturation of the spatial EMF distribution occurs when the number of active DL
beams is above 25%. For UL, within 5m radius of the user’s location, the
average E-field may increase from 2.40 to 3.98 V/m. (increment of 66%) if the
number of active users in the area increases from 25% to 100%. Moreover, when
100% of the users are actively transmitting, there is only a 10% probability
that the SAR may exceed 0.06 W/kg (or 3% of the ICNIRP’s limit).
Dense urban environment; EMF exposure; Multiuser beamforming; Power density; Specific absorption rate
In order to support the demands for higher throughput
and improved quality-of-service (QoS) specified in the 5G and beyond standards,
new features such as beamforming
1.1. Related Works
This
section discusses previous works relevant to EMF exposure evaluation of
beamforming. In (Chiaraviglio et al., 2018), the authors analyzed the EMF levels of realistic
pre-5G scenarios using ray tracing. However, only standard fixed beam BS
antenna and frequency less than 3 GHz are considered. In (Chiaraviglio et al., 2021), the
authors investigated the EMF impacts of the pencil beamforming technique of 5G
BS in terms of power density (PD). However, the proposed EMF evaluation
procedure is only applicable for localization-based beamforming using the
standard 3GPP propagation model (ETSI, 2019). Downlink EMF exposure generated by 5G beamforming
antennas is investigated in (Noé & Gaudaire, 2021) using ray-tracing. The authors compared the influence
of different beamforming approaches on electric field exposure levels in urban
environments. However, the frequency used is not specified. EMF exposure
evaluation methodology can be found in international standards such as IEC
62232 (IEC, 2017) and ITU
K.91 (ITU, 2020) for
assessing the EMF level from individual base stations and base station sites
based either on calculations or measurements.
These evaluation guidelines’ primary purpose is to assess EMF compliance
boundaries of cellular BS and devices. Hence, highly simplified models such as
the free-space formula and time-averaged antenna patterns are usually used for
calculating exclusion zones. This methodology is unsuitable for accurate
spatial mapping of EMF level for the entire site area. Field measurement using
live commercial 5G site is another way for evaluating the EMF exposure (Aerts et al., 2021; Colombi et al., 2020). However, this approach has some key challenges such as the
availability of site and equipment, detailed BS and device parameters, dynamic
traffic and multiuser conditions (Adda et al., 2020). Moreover, fine-grained analysis for the entire site
area is not feasible via measurement. It is well known that mm-wave
transmission is very sensitive to terrain irregularity and buildings or
obstacles geometry. As a result, the variability in EMF levels from one
location to another in an urban scenario can be very significant. In these
conditions, a deterministic technique such as ray-tracing is very promising for
effective evaluation of the EMF exposure. However, the EMF evaluation framework
based on ray-tracing is still not well defined, and considering the different
exposure modes of 5G compared to legacy systems, further work is required to
provide a precise spatial characterization of the EMF impact. Specifically, the
impacts of practical mm-wave beamforming abnormalities such as dual main beam
and the combined effects of multiuser beams in a realistic 3D environment on
the EMF level are still less understood.
A lack of study also considers the total uplink exposure in a realistic
densely populated urban scenario. In contrast to the existing works, the
following key novelties are introduced in this work: 1) we design a framework
based on ray-tracing that allows 3D scenarios definition and synthesis of
mm-wave massive MIMO beams according to the UE’s multipath channel
characteristics; and 2) we characterize the spatial EMF distribution with
different variation of user distribution, user density as well as average and
worst case conditions.
2. System Model & Proposed Framework
5G will employ a set of radically new technologies, such as large-scale MIMO antenna arrays, precise beamforming, and mm-wave communications. These new features will substantially change the radio access part of 5G networks compared to the legacy pre-5G standards. Therefore, new radio access models integrating these 5G features must be developed for EMF exposure evaluation. Figure 1 shows the flowchart of the proposed EMF evaluation framework based on ray-tracing.
Figure 1 Flowchart of the proposed EMF evaluation framework
The details of the proposed evaluation
framework are described in the following subsections:
2.1.
Modelling 5G System and Scenario
A 3D map is needed to create
a dense urban environment for ray-tracing simulation. For example, as shown in
Figure 2a, a small cell scenario in the urban city of Rosslyn, Virginia, is
considered. The size of the area considered is 0.09 km2. Based on (Ibraiwish et al., 2022), for a
dense urban environment, the expected population size in this area is 123
people. By randomly distributing the users’ locations, 93 users are found to be
located outdoors, and 30 users are located within the buildings (or indoor). In
this work, only outdoor users are considered for EMF exposure evaluation.
Figure 2a shows the 5G base station (green dot) positioned on the top of a lamp
post on a road divider in a major traffic intersection. The users (red dots)
are randomly distributed within the cell. The EM properties of the materials
such as building, and vegetation are defined according to the recommendation of
previous works in the literature.
We consider a massive MIMO
base station operating at 28 GHz in a densely populated urban scenario.
Multiuser beamforming technique is employed at the BS to support multiple users
with one data stream per user. Each data
stream transmission assumes an 8x8 transmit antenna array with both vertical
and horizontal polarizations (total 128 antenna elements). Ray tracing is used
for determining the channel state information at the transmitter by simulating
the interactions between the transmitted signal and the propagation channel. To
ensure accuracy and reproducibility of the results, commercial software called
Wireless InSite (Remcom, 2016) is utilized. For uplink, a single antenna is assumed
for the UEs.
2.2. Ray-Tracing
Simulation
3D ray-tracing simulation is
performed using a combination of Shooting and Bouncing Rays (SBR) technique (Schuster & Luebbers, 1996a; 1996b) and
Geometric Optics (GO) and Uniform Theory of Diffraction (UTD) methods. Ray
paths are first launched and traced from the source point. Specularly reflected
rays from the building walls and objects are continuously traced up to the
maximum number of reflections or when the rays hit the study area boundary. GO,
and UTD are then employed to evaluate the complex electric fields and received
power associated with each ray path. This ray-tracing approach is used to
determine the following parameters:
2.2.1.
MIMO Channel Coefficients
The complex-valued channel coefficient between the nth antenna element to the kth user can be expressed as (Remcom, 2016) (equation 1),
where Gk[n] is the ratio of power received by user k and power radiated by element n when all other elements are turned off and is the phase (in radians) of the voltage across a matched load at user k under the same conditions. The gain Gk[n]and phase take into consideration the coherent sum of all the propagation paths in a complex urban environment from antenna element n to user k.
2.2.2. DL Received Power
For DL, the received power can be expressed as (Remcom, 2016) (equation 2),
where (.)H denotes conjugate transpose, is the complex channel coefficients vector and wkis the precoding weights vector for user k. The precoding weights depend on the beamforming technique used.
2.2.3. UL Electric Field Intensity
For UL, the SBR method
finds the propagation paths from all the transmitters (or UEs) to the receiver
points. Once the propagation paths are determined, the GO and UTD are used to
evaluate the electric field for each path. The computation of the electric
field depends on whether the path is a direct line-of-sight, reflected, or
diffracted path. The details of the calculations are available in (Remcom, 2016). The
results are coherently summed up to produce the total electric field intensity
at the receiver point.
2.3.
Beamforming Technique
The maximum ratio transmission (MRT) technique is considered so that the
maximum power can be delivered to the intended user using beamforming for EMF
exposure evaluation. To this end, for user k,
the weights of the antenna elements are set to be proportional to the channel
values of the respective elements. In other words, the precoding weights vector
for user k is given by (equation 3),
where is the power transmitted towards
user k.
2.4. Downlink and Uplink EMF Computation
The EMF exposure due to downlink transmission is evaluated using total
power density. The power received by the receiving antenna can also be written
as (Rappaport, 2002) (equation 4),
where is the effective area or aperture of the receiving antenna and is the power density of the radiation in any particular direction from the antenna. For an isotropic antenna, the effective area is given by (equation 5),
|
where is the wavelength of the signal. In this work, the total received power for the entire study area is first determined using ray tracing simulation. Then the power density is computed by (equation 6),
|
On the other hand, the EMF exposure due to uplink transmission is
evaluated using the specific absorption rate (SAR) and electric field strength
(E-field). SAR measures the energy from electromagnetic sources absorbed per
unit mass by human tissues. It can be expressed as (Nasim, 2019; Remcom, 2016) (equation 7),
|
where is the conductivity of tissue in S/m, E() is the electric field intensity in V/m, and is the mass density of tissue in kg/m3.
Table 1 shows the simulation parameters considered
in this paper for the 5G dense urban network. For the downlink, the
complex-valued path gains for each sub-channel between the BS and the users
were obtained from a realistic ray tracing simulator (Wireless Insite) to
calculate the beamforming (or precoding) weights. These precoding weights are
calculated using the MRT beamforming technique to ensure that the users receive
the maximum power. Then, the received power for the entire study area is
simulated and used to evaluate the EMF exposure level. Figure 2b illustrates
the received power heatmap where the beam is directed towards a user near the
map’s middle. In the simulation, the precoding weights are calculated to direct
93 beams toward the 93 outdoor users. The distance between two adjacent sample
points on the map is 1 m. At these sample points, the received power and the
electric field strength are simulated to produce the EMF heatmap. The power
density is then calculated to evaluate the downlink exposure level.
Table 1 Simulation
parameters
Parameter |
Description |
Remark |
5G System & Scenario | ||
Frequency |
28 GHz |
- |
BS antenna |
8x8 array per data stream |
Total 93 data streams |
BS height |
10 m |
|
BS Tx power |
30 dBm per data stream |
(Skidmore
et al., 2016) |
UE Tx power |
20 dBm |
(Skidmore
et al., 2016) |
UE antenna |
Single halfwave dipole |
(Skidmore
et al., 2016) |
UE height |
2 m |
|
Conductivity of tissue |
21.86 (S/m) |
(Lak et al, 2021) |
Mass density of tissue |
1041 (kg/) |
(Lak et al, 2021) |
Ray-Tracing Parameters | ||
Number
of reflections |
6 |
(Skidmore
et al., 2016) |
Number
of Paths |
25 |
(Skidmore
et al., 2016) |
Ray
Spacing |
0.15 |
(Skidmore
et al., 2016) |
Ray
tracing Technique |
SBR |
(Skidmore
et al., 2016) |
Propagation
Model |
Full 3D (X3D) |
(Skidmore
et al., 2016) |
EM Properties of Materials | ||
Material |
?r |
?[S/m] |
Buildings (Concrete) |
5.31 |
0.8967 |
Vegetation-Leaf |
26 |
0.39 |
Vegetation-Branch |
20 |
0.39 |
Figure 2 (a) Location of base-station and random users’
locations in a dense urban environment, (b) Received power heatmap for a single
beam directed towards a user located near the middle of the map
Figure 3 shows the average and peak
values of the total power density versus distance from the BS.
Figure 3 Downlink power density
Figure 3 shows the peak and
average PD for the extreme case where all the 93 beams are activated
simultaneously. The same figure shows the maximum limit given by ICNIRP and the
case where only one beam is directed towards a single user. It is observed that
the power density is mainly dependent on two factors: the distance from the
base station and the concentration of the users in a specific area. Generally,
the power density drops as the distance increases. However, at some locations
far away from the base station, the total power densities are higher compared
to those of nearer locations. This is due to the higher density of UEs
operating near each other in those areas. Many beams are directed toward the
same location at the same time, and this causes elevated EMF levels. Based on
Figure 3, the peak PD increases from 6.65 to 24.92
Figure 4a shows the spatial
distribution of the total power density in the dense urban environment due to
massive MIMO multiuser beamforming when all the beams are activated
simultaneously.
Figure 4 (a) Spatial distribution of the total downlink power
density, (b) ECDF of power density
Compared to Figure 2b, it is obvious
that the EMF level in the study area has increased substantially when each user
is actively served by one beam from the BS. This worst-case scenario shows that
almost all the areas previously with very low EMF exposure (when only one beam
is activated) are now experiencing high EMF exposure. In Figure 4b, the
empirical cumulative distribution function (ECDF) of total PD is plotted. It
can be observed saturation of EMF exposure starts to occur when the number of
active users in the area is above 25%.
For UL, the EMF exposure level is
evaluated using the electric field strength and the SAR metrics. Figure 5a
shows the spatial distribution of the total electric field strength within the
densely populated urban environment considered in this study. Figure 5b shows
the E-field strength versus distance from the user for different percentages of
actively transmitting outdoor users.
Figure 5 (a) Spatial distribution of total E-field strength
for uplink, (b) Total E-field strength versus distance from the user
It is observed that the electric field
strength decreases with the distance from the user’s position. However, the
exposure level is also dependent on the percentage of active UEs radiating at
the same time and the density of users within a certain area. Considering a 5 m
radius from a user, the average E-field may increase from 2.40 to 3.98 V/m (or
66%) when the number of active users increases from 25% to 100%. The total
uplink E-field did not reach the limit set by ICNIRP (ICNIRP,
2020), which is 36.5 V/m. The exposure
level is significantly higher for some places where the users are more
concentrated (see Figure 5a). This leads to the observation that UL EMF
exposure may increase with the densi?cation of wireless devices operating near
each other at the same time.
Since SAR is electric field strength
dependent, higher electric field strength results in higher SAR. The spatial
distribution of total SAR due to UL is shown in Figure 6a.
Figure 6 (a) Spatial distribution of total SAR for uplink, (b)
ECDF of total SAR
Figure 6b presents the ECDF
of SAR levels within the considered study area (with total of 93 active outdoor
users). Based on Figure 6b, when all users are actively transmitting, there is
a 10% probability that the SAR may exceed 0.06 W/kg, which is equivalent to
only 3% of the ICNIRP’s limit. The spatial EMF exposure level increases
gradually with the increase of the number of active users from 25% to 100%.
This is mainly due to the omnidirectional antenna employed by the UEs.
This
paper investigates the EMF exposure level of a 5G network employing massive
MIMO multiuser beamforming in downlink transmission based on the total power
density (PD) exposure metric. The EMF exposure due to uplink transmission is
also investigated using total electric field strength and specific absorption
rate (SAR). A ray-tracing-based EMF evaluation framework is proposed to exploit
detailed information of the network scenarios, including 3D building geometry,
EM characteristics, multipath propagation, user locations, and beamforming
radiation pattern, to effectively evaluate spatial variations of the EMF
levels. Following this evaluation procedure, the impact of different user
densities and distributions has been evaluated in terms of PD and SAR. Both
worst-case and average-case scenarios are analyzed for a dense urban
environment using the proposed framework. Based on the results, all the metrics
did not exceed the limits set by ICNIRP. However, the exposure level is
significantly increased by the densification of the users and the distribution
of the radiating user devices. The exposure level may increase further for an
environment with denser users. This study contributes to understanding the
expected EMF exposure level in the entire densely populated urban area where 5G
uplink and downlink transmissions are considered. For future investigation, it
is recommended to study the EMF exposure effects of other digital beamforming techniques
and the hybrid analog/digital beamforming.
The Malaysian Ministry of Higher
Education supported this work through the Fundamental Research Grant Scheme
under Grant FRGS/1/2020/ICT09/MMU/02/1.
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