Post on 26-Feb-2023
Multiobjective Optimization for Low SAR Antenna Design
Ibrahim Elshafiey, Abdel-Fattah Sheta, Saeed Aldosari, Majeed A. Alkanhal, and Saleh A. Alshebeili Electrical Engineering Department and Prince Sultan Advanced Technologies Research Institute (PSATRI)
King Saud University, PO Box 800, Riyadh 11421, Saudi Arabia
Abstract- With the widespread use of wireless communication
systems, efforts are directed to reduce the human exposure to RF
radiation. Multiobjective optimization is becoming essential to
achieve satisfactory antenna performance whilst minimizing the
radiation level in the human body. A technique based on the genetic
algorithm is proposed to optimize a multiobjective function
involving weighted goals related to the antenna performance and
the near-field radiation pattern inside the human body.
Electromagnetic simulation based on FDTD formulation is
performed using SEMCAD X. Dispersive material characteristic is
chosen for the human head phantom. Results illustrate the
enhancement introduced by this technique of antenna design that
could be used in multifunction communication systems.
Keywords-Antenna Design, SAR, Multiobjective Optimization,
Dispersive Material.
I. INTRODUCTION
The biological effects of RF radiation has attracted wide
interest in the research community due to the widespread use
of mobile communication systems. RF radiation was
traditionally thought to have only thermal effects in the
frequency ranges used in communication systems. Recent
studies, however, investigate other forms of effects such as
genetic changes in human cells [1].
Exposure guidelines attempt to define the level below
which health effects should not occur. Specific absorption
rate or SAR is the absorbed dose rate and it is thus the time
derivative of the incremental energy (dW) absorbed by or
dissipated in an incremental mass (dm) contained in a volume
(dV) of a given density (ρ):
𝑆𝐴𝑅 =𝒅
𝒅𝒕 𝒅𝑾
𝒅𝒎 =
𝒅
𝒅𝒕 𝒅𝑾
𝝆𝒅𝒗 (1)
It is usually straight forward to determine the field
distribution for a specific SAR value, if the exposed body is
far enough from the field source, which is the case, for
example, of the public exposure to base station fields. This
concept cannot easily be applied if the body is very close to
become in the near-field of the source, such as the case of a
user holding a mobile handset.
An obvious approach towards dosimetric analysis is to
determine the SAR in phantoms simulating human head or
body containing a tissue-equivalent material. SAR can be
measured by recording the local temperature values. SAR is
proportional to temperature increase if the effects of heat
diffusion can be neglected [2].
Another way of determining the SAR is by measuring the
electric field (E) inside the tissue-simulating material. SAR
is given in terms of conductivity and density as
𝑆𝐴𝑅 =𝜎 |𝐸|2
𝜌 (2)
where is the conductivity of the tissue.
SAR can be reduced using advanced design techniques of
antenna. Antenna design is often a complex optimization
problem involving the selection of many interrelated
parameters such as the geometric parameters of the antenna
to meet multiple design objectives such as desired bandwidth,
return loss and beamwidth. Reduction of SAR is a new
optimization objective of antenna design. The search surface
in such problems is often multimodal with many local
minima and maxima. In many cases, it could also be
discontinuous.
This study presents a design of an antenna that operates at
four frequency bands: 2.45 GHz, 3.5 GHz, 5 GHz and 5.8
GHz. This antenna could thus be operated in multifunction
systems that could operate Bluetooth, WiFi and WiMax
systems, for example.
II. ANTENNA OPTIMIZATION TECHNIQUES
Optimal antenna design for mobile communication
systems started to take into consideration the interaction of
the antenna with the human body in addition to the basic
antenna parameters. Techniques used in antenna design can
be categorized into local or global search methods. Local
search methods such as the simplex techniques and gradient-
based techniques usually fail to provide satisfactory results
since most of them are suitable only for simple optimization
problems involving smooth unimodal search surface.
Global optimization techniques can avoid being trapped in
local minima/maxima and hence they are adopted in many
complex optimization problems. Many global optimization
techniques are inspired by natural phenomena and are
becoming increasingly popular and finding growing
applications in different areas including electromagnetics
(EM). Nature inspired methods are not based on
mathematical formulation; rather, they try to mimic natural
selection and evolution processes. Unlike random-walk,
nature-inspired techniques follow a certain well-defined
procedure that help guide the search towards the optimal
parameters. Examples of these methods include simulated
annealing, particle swarm optimization, and evolutionary
algorithms.
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Simulated annealing (SA) is an optimization technique
that tries to simulate the physical process of annealing,
through which a material is made less brittle by gradual
heating and cooling cycles. Simulated annealing methods
have been applied in various antenna optimization problems
as in [3].
Particle Swarm Optimization (PSO) has been introduced
into EM applications recently [4]. It is a stochastic nature-
inspired computation technique based on the concept of
emulating the intelligent movement of swarms of bees
looking for feeding locations. In contrast to techniques that
rely on a competitive model (survival of the fittest), PSO is
based on a cooperative model. Relying on the social
behavior of a swarm of bees, fish and other animals, the
concept of PSO has proved to be useful in solving
unconstrained global optimization problems.
Evolutionary techniques are increasingly being
implemented in electromagnetic design tools because of their
flexibility, versatility and ability to handle complex
multimodal search spaces [5]. These techniques are
discussed next.
III. EVOLUTIONARY ALGORITHMS
The search process in evolutionary algorithms (EA)
usually starts with a random initial guess, and then the
parameters are evolved using an evolutionary process. This
evolutionary process is guided by a fitness function, which
quantifies the goodness of the current solution and helps
direct the search towards the optimal solution.
Three main categories of EAs can be identified in the
literature: genetic algorithm (GA), evolution strategies (ES),
and evolutionary programming (EP) [6]. In terms of the
evolution level, GA operates at the genetic level, ES at the
individual level, while EP operates at the species level. In
addition, one of the most important differences between these
algorithms is the choice of the variation operator, which
directly affects the evolution process. For instance, since GA
operate at the genetic level, evolution is carried out using a
mix of crossover and mutation of genes, where the former is
the dominant operator. It requires two parents to produce an
evolved offspring. In contrast, EP operating at the species
level is entirely based on mutation, where one parent is used
to produce evolved species.
Of the three EA multiagent stochastic search methods, GA
is the most popular within the electromagnetic community.
Results have shown that ES and EP can be more efficient in
problems involving multimodal optimization problems
especially when the optimization parameters are highly
correlated [6]. Since its introduction in 1975 by John
Holland the genetic algorithms (GA) has found a wide spread
use in many fields including communications and
electromagnetics. An example of the use of GA in antenna
design is found in [7], where GA optimization resulted in
excellent bandwidth and gain properties with good
impedance characteristics that outperformed conventional
designs. The GA was also applied successfully in the design
of the microstrip patch antenna as in [8].
The genetic algorithm is based on the concept of survival
of the fittest outlined in Fig. 1. The process starts with a
random population corresponding to an initial guess. The
population comprises a group of chromosomes from which
candidate solutions are selected. Each chromosome in the
population represents a candidate solution. The chromosomes
are represented by strings of binary bits (genes), which
represent the value of the optimization parameters. The
goodness of all chromosomes in the population is assessed by
evaluating the fitness function then a group of chromosomes
is selected from the population. These parent chromosomes
are used to generate the offspring after which their fitness is
evaluated similarly. A replacement strategy is then followed
to replace chromosomes in the current population with their
offspring. This process is repeated until a desired criterion is
achieved.
GA-based algorithms usually suffer from the high
computational complexity. This is because the GA performs
its searching process via a population-to-population instead
of point-to-point search. Therefore, at each optimization
(evolution) step, the fitness function is evaluated for all
members of the population instead of just one.
IV. MULTIOBJECTIVE OPTIMIZATION
Multiobjective optimization is becoming essential in the
antenna design of modern communication systems. Various
commercial packages are becoming equipped with
optimization tools such as SEMCAD-X, which we adopt to
conduct the optimization analysis and electromagnetic
modeling simulation [9].
Recombination
END
Initial Population
Mutation
Reproduction
Fitness Evaluation
Fitness Evaluation
STOP?
yes
no
For Each
Cromosome
Fig. 1. Outline of the Genetic Algorithm
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SEMCAD-X is based on the FDTD method and is
specialized in the computation of SAR. One of the most
attractive features of SEMCAD-X is the ability to conduct
multiobjective optimization. This environment is used to
develop muli-frequency band antennas which minimize SAR
value in the tissues of the head of the system operator.
V. MULTIFUNCTION SYSTEM ANTENNA
We consider the design of an antenna that can be used in
multifunction mobile communication systems. A suitable
prototype is presented in [10] where an ultra-wide band UWB
antenna was used in microwave imaging. As shown in Fig. 2,
the antenna consists of a slotted patch implemented on one
side of the dielectric substrate and a partial ground in the
form of L-shaped on the other side. The antenna is based on
a prototype UWB antenna presented in [11] which is a planar
antenna manufactured over a single PCB of size 25 mm x 25
mm x 1.5 mm, which achieves operation in the range 2.9-11.6
GHz.
The L-shaped ground of the proposed antenna structure
consists of two perpendicular rectangular structures.
Analysis of antenna structure revealed that changes in main
shape of antenna deteriorate performance while the L-shaped
ground is easy to optimize.
Optimization is performed to adapt the antenna structure
to achieve a set of requirements. Tree algorithm is used to
avoid the destruction of good schemata during the
optimization process by estimating the chromosome
probability density and giving more chances to those
chromosome distributions whose fitness is higher through the
use of dependency trees,. Optimization parameters are chosen
such that elitism (percentage of samples copied) is 30% and
mutation probability is 1%. Population size is 30.
Five parameters are chosen for optimization including the
length (L1) and width (W1) of the horizontal arm in addition
to the length and width L2 and W2 of the small vertical arm
shown in Fig. 3. The center (C1) of the horizontal arm along
the horizontal axis is also set for optimization. Table 1
presents the range of the chosen parameters. Number of bits
is chosen to be 8 for all optimization parameters, which
indicates the resolution of values given in the table.
Fig. 2. UWB planar structure. Grid divisions are 1x1 cm2.
Fig. 3. L-shaped ground structure implemented in the optimization.
The optimization goal is set so that the S11 parameter in
the input port is less than -10 dB in the frequency ranges [2.3-
2.7], [3.3-3.7] and [5.0-6.0] GHz.
Best five chromosomes and their fitness values are given
in Table 2. The S11 value corresponding to the best-obtained
chromosome is presented in Fig. 4 along with the goal values.
Fig. 4. S11 corresponding to the best chromosome
Table 1. Optimization parameters
Parameter Minimum value
Maximum value Resolution
C1 22.5 27.5 0.0195313
L1 45 55 0.0390625
W1 8.1 9.9 0.00703125
L2 11.7 14.3 0.0101562
W2 2.7 3.3 0.00234375
Table 2. Best five chromosomes for antenna in free space.
C1 L1 W1 L2 W2 Fitness
26.559 54.490 8.135 14.157 2.940 91.320
27.343 54.529 8.142 13.566 2.825 87.549
26.088 54.804 8.121 12.944 3.274 87.349
26.931 53.784 8.185 14.188 3.274 83.802
27.480 54.569 8.255 14.096 2.754 81.703
L1
W1
L2
W2
C1
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VI. OPTIMIZATION OF SAR
Multiobjective optimization is performed to enhance the
antenna characteristics while minimizing the RF deposition in
human head. A model is built in which the antenna is set
close to SAM phantom of human head as shown in Fig. 5.
Same optimization parameters presented in Table 1 are
used. The optimization goal is set so that the S11 parameter in
the input port is less than -15 dB in the frequency ranges [2.3-
2.6], [3.3-3.6] and [4.9-6.0] GHz. SAR is set to be minimized
below 1.5 W/kg at frequencies 2.5, 3.5, 5.0 and 5.8 GHz.
Fig. 5. Antenna close to human head phantom.
Since the human tissue parameters are frequency
dependent, dispersive material characteristic is chosen for
human head phantom. Debye model with single pole is
chosen such that the complex permittivity 𝜀 is given as
𝜀 𝜔 = 𝜀∞ +(𝜀0−𝜀∞ )𝐴
1+𝑗𝜔𝜏 (3)
where 0 is the static permittivity and is the permittivity at
infinity. A and are the magnitude and relaxation time
corresponding to the pole. The parameters given in Table 3
are fitted to Equation 3 to obtain the various parameters.
VII. RESULTS AND DISCUSSIONS
Best five chromosomes and their fitness values are given
in Table 4. The SAR values corresponding to the best
chromosome are given in Table 5 averaged on 1 gram and 5
gram of the tissue. The S11 values are presented in Fig. 6.
SAR map inside human head phantom at 2.5 GHz is shown in
Fig. 7.
Figure 8 illustrates the field sensor region that includes the
antenna and part of the human head phantom. Results of the
electric field maps within the field sensor region
corresponding to best obtained chromosome are shown in
Fig. 9, where the input power is set to 1 W. 0-dB
corresponds to 8029 V/m for 2.5 Ghz, 6473 V/m for 3.5 Ghz
and 8175 V/m for 5 GHz. The figure reveals the increase of
attenuation of the field in the human head with increase of
frequency.
Figure 10 presents the current density at different
frequency values. 0-dB corresponds to 94792.6A/m2 for the
antenna structure and 59219.7A/m2
for the ground at 2.5
GHz, 68408.9A/m2 (antenna) and 63140.6A/m
2 (ground) at
3.5 GHz, and 51735.8A/m2 and 40167.4A/m
2 at 5 GHz. The
figure reveals the role of different arms of the slotted patch
and in different frequencies. It also illustrate the role of each
of the two arms of the L-shaped ground in different
frequencies.
Table 3. Human head material parameters
Frequency
(GHz)
Relative Permittivity Conductivity
(S/m)
0.15 42.1883 0.816924
0.3 42.1702 0.828701
0.45 42.1399 0.848304
0.83 42.0096 0.932686
0.9 41.9773 0.953601
0.91 41.9725 0.956724
1.45 41.6362 1.17454
1.61 41.5087 1.25715
1.8 41.3413 1.36557
2.0 41.1469 1.49147
2.45 40.6448 1.81674
3.0 39.9192 2.28677
5.8 34.9284 5.51948
Table 4. Best five chromosomes for antenna close to human phantom.
C1 L1 W1 L2 W2 Fitness
27.422 53.078 8.34 13.484 2.778 91.08
27.069 54.804 8.884 12.322 2.879 90.77
27.186 52.961 8.926 13.882 3.173 90.446
27.304 51.51 8.538 13.637 2.9 90.356
27.245 49.039 8.919 14.106 2.825 89.751
Table 5. SAR values in the human phantom.
Freuqency Mean SAR
(W/kg)
Minimum SAR
(W/kg)
Maximum SAR in
(W/kg)
Maximum SAR
averaged
on 1g (W/kg)
Max SAR
averaged
on 5g (W/kg)
2.5 GHz 1.92391 0.157442 10.4047 5.59799 3.78053
3.5 GHz 0.561778 0.0146166 4.057678 2.11378 1.416
5 GHz 0.655285 0.030957 3.20819 1.67999 1.12386
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Fig. 6. S11 corresponding to the best chromosome when antenna is close to
the human head phantom.
Fig. 7. Map of SAR inside human head phantom at 2.5 GHz. 0-db
corresponds to 10.4 W/kg.
Fig. 8. Field sensor including the antenna and part of the phantom
Fig. 9. Electric field map at 2.5 GHz (top), 3.5 GHz (middle) and 5 GHz (bottom).
2 3 4 5 6 7 8 9 10 11 12 13 14-22
-20
-18
-16
-14
-12
-10
-8
-6
-4
-2
Frequency (GHz)
S11
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Fig. 10. Current density maps at 2.5 GHz (top), 3.5 GHz (middle) and 5
GHz (bottom
VIII. CONCLUSIONS
Multiobjective optimization is becoming essential to
achieve satisfactory antenna performance, particularly in
minimizing the radiation level in the human body. A
technique based on genetic algorithms is presented, where a
multiobjective function is formed by weighted goals related
to the antenna performance and the near-field radiation
pattern inside the human body. This function is minimized
using the genetic algorithm. The inherent parallel nature of
the genetic algorithms can help eliminate the required high
computational complexity through parallel processing.
Electromagnetic simulation is performed under SEMCAD-
X environment. Since the human tissue parameters are
frequency dependent, dispersive material characteristic is
chosen for simulating head tissue. Debye model with a single
pole is assumed. Presented results illustrate the power of the
developed techniques in optimizing antenna design,
particularly for challenging problems requiring the reduction
of SAR of an antenna of a multifunction communication
system operating at various frequency bands, close to
dispersive biological material.
ACKNOWLEDGEMENT
This research is funded by King Abdulaziz City for
Science and Technology (KACST), Research Grant: MT-2-5.
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