=Paper=
{{Paper
|id=Vol-2145/p05
|storemode=property
|title=Hybrid Neural Network Optimization for Feed Point Determination in Antenna Design
|pdfUrl=https://ceur-ws.org/Vol-2145/p05.pdf
|volume=Vol-2145
|authors=Umut Özkaya,Levent Seyfi
}}
==Hybrid Neural Network Optimization for Feed Point Determination in Antenna Design==
https://ceur-ws.org/Vol-2145/p05.pdf
Hybrid Neural Network Optimization for Feed Point
Determination in Antenna Design
Umut Özkaya Levent Seyfi
Department of Electrical and Electronics Engineering Department of Electrical and Electronics Engineering
Selçuk University Selçuk University
Konya, Turkey Konya, Turkey
e-mail: uozkaya@selcuk.edu.tr e-mail: leventseyfi@selcuk.edu.tr
Abstract—In this paper, coaxially feed rectangular microstrip method. The inputs of the artificial neural network are
antenna is designed for WIFI communication in accordance with dielectric constant, height, TE and TM modes of the material;
IEEE 802.11a standard between 5.15 GHz and 5.725 GHz. on the other hand, resonant frequency is the output of neural
Feeding position of coaxial probe significantly affected antenna network [2]. Microstrip patch antenna was designed for wide
characteristics. Optimum feeding point should be selected in 2-D band applications with dielectric material thickness of 2 mm.
patch plane on the purpose of better antenna characteristics. The The operating frequency of the microstrip patch antenna with
model is used to solve the optimization problem. It has three 10.5 to 12 GHz bandwidth was tried to be calculated by the
input variables which are antenna parameters as resonance genetic algorithm [3]. Hybrid artificial neural networks
frequency, bandwidth and return loss; on the otherhand, two
techniques and fuzzy logic methods were used to calculate the
output such as x and y coordinates of feeding position. Also, error
function is updated by proposed artificial intelligence algorithms.
operating frequencies of microstrip antennas. The hybrid
Unlike conventional methods, contemporary artificial intelligent method based on the least squares method with the
algorithms have been proposed for the antenna design. Genetic backpropagation algorithm was performed for dimension
Algorithm (GA), Spider Monkey Optimization (SMO) and Grey optimization of square, circle and triangle microstrip patch
Wolf Optimizer (GWO) are preferred for optimization. antennas [4]. Operating frequency of the microstrip patch
According to comparison of these results, optimal antenna for antenna with coaxial feed was determined by artificial neural
WIFI Protocol is designed. network methods. The inputs of the artificial neural network
were the antenna's width and height, and the output was the
Keywords— Microstrip Antenna, WIFI Communication, operating frequencies in the dual band [5]. The bandwidth of
Artificial Neural Network, Artificial Intelligence Algorithm, the microstrip patch antenna with rectangular geometry was
Optimization. tried to be optimized by using artificial neural network. Error
rates of simulation and network results were compared with
I. INTRODUCTION each other [6]. Forward feedback propagation algorithm is used
to optimize and microstrip antenna parameters with square and
In the recent years with the development of the technology, rectangular pitch are provided. The dimensions of the
the use of microstrip patch antenna has gradually increased in microstrip antenna were designed as network inputs and the
spacecraft, doppler and navigation radar, satellite operating frequency was optimized as the network output [7].
communication, mobile radio and guided missiles. Microstrip A particle swarm optimization algorithm was used to determine
antennas have several advantages over many known the patch sizes of a multi-layer microstrip antenna that can
conventional antennas. These advantages are low profile, operate in the X and Ku band [8]. A microstrip patch antenna
changeable polarization with feeding position, integrated with with many slots for wireless communication was designed by
solid-state equipment and compatible with co-planar surface multilayer perceptron artificial neural network model [9].
[1]. Hybrid artificial neural networks, which is a combination of
Great strides in the electronics sector provide more radial based function and backpropagation algorithm, was used
functionality and size reduction for especially communication for design of acoupled microstrip antenna. The performance of
devices. Besides, demands for multiple applications (GPS, the hybrid network was compared with other types of network
GSM, WIFI) in a single device has caused designers to focus results [10]. Conjugate gradient artificial neural network model
more on microstrip antenna. Many applications of artificial was used for determination of the operating frequency in
neural network and artificial intelligence algorithms exist in circular microstrip patch antenna [11]. The truncated edges of
literature. The results obtained by using the feedback square microstrip antenna was designed with artificial neural
multilayer perceptron network for the design of the equilateral network. Levenberg-Marquardt algorithm with three hidden
triangle microstrip antenna were compared with conventional layers was chosen as an artificial neural network method [12].
Artificial neural networks were used for fractal antenna design.
Copyright held by the author(s).
28
Analysis of the error rates of the network results were carried c
out with the Generalized Regression Neural Networks (GRNN) W
[13]. Variables such as operating frequencies, gains, r 1
2f 0
directionality, antenna efficiency and radiation efficiency in the 2
dual band were assigned to the inputs of the different artificial
neural network based algorithms and height of gap between the
ground plane and the dielectric material was computed [14]. Leff
c
The design of microstrip antennas with rectangular and circular 2f 0 eff
patches for wireless communication applications was
implemented with particle swarm optimization technique. The
inputs of the optimization technique are the operating
frequency, the dielectric constant and the height of the ( eff 0.3)( w / h 0.264)
L 0.412h
dielectric material. [15]. Sathi et al. used to be processed ( reff 0.258)( w / h 0.8)
moments matrices optimally with the genetic algorithm,
ensuring that the simulation and test results are in harmony
with one another in the antenna design [16]. Antony et al. L Leff 2L
proposed PSO as a design tool in a microstrip antenna array
created by a parasitic method. With their design, IEEE 802.11a
WLAN achieves a multi-directional radiation pattern and
reflection coefficient of <-10 dB in the 5-6 GHz band [17]. Lg 6h L
Amir et al. calculated resonance frequency and bandwidth in
the rectangle microstrip antenna design. Faster and more
accurate results were obtained by optimizing the moments
method with Bacterial Search Optimization [18]. Arunava et al. Wg 6h W
applied Cuckoo Search algorithm to increase the bandwidth of
the microstrip patch antenna running in the X band [19]. W×L has been computed as 21.56 × 17.46 mm2 for
Metaheuristic optimization method have shown a great patch and on top of that Wg×Lg has been calculated as 41.8 ×
development and its applications has been carried out in many 35.6 mm2 for ground plane. The antenna designed can be
fields for the last twenty years. Genetic, Differential Evolution, excited by coaxial feed as Figure 1. It can be at any position to
Gravitational Search and Teaching-learning based optimization match with input impedance.
algorithms are given examples for some commonly used
metaheuristic optimization algorithms [20]. In this study, It is
introduced that how to design microstrip antenna in Section II.
Proposed Artıfıcıal Neural Network algorithm is described in
Section III. Genetic Algorithm, Spider Monkey Optimization
and Grey Wolf Optimizer, which are based on swarm
intelligence and are known as a novel optimization algorithms,
are used to determine coaxial feeding position of microstrip
antenna for Wi-Fi protocols in Sections IV. The experimental
results drawn in Sections V. Section VI inserts the conclusion
part.
II. MICROSTRIP ANTENNA DESIGN
Basic microstrip patch antenna design consists of the three
main parts. These are patch plane, dielectric substrate and
ground plane. FR4 material (ɛr=2.2) is used in dielectric
substrate of proposed microstrip antenna for Wi-Fi protocol.
The thickness of this material is determined to be 1.58 mm.
The thickness of the copper patches on the dielectric substrate
is 38 μm. The operating frequency of the antenna is designed
around 5.38 GHz and is suitable for IEEE 802.11a protocol.
Patch and ground plane size are designed in accordance with
the following formulas in which L, W and Lg, Wg represents
patch and ground dimension respectively:
0.5
r 1 r 1 h
eff 1 12
(1)
2 2 W
Fig. 1. Top and Side View of Coaxial Feed Microstrip Patch Antenna
29
Pseudo code of GA is given in Fig. 2. CP represents
crossing-over point and should be smaller than chromosome
Typically, even if cos2(πy0/h) formula suggests matching size. Mutation rate is abbreviated as MR. It is important for
with 50 ohm impedance, there will be some feed position to diversity in new generation.
obtain greater return losses at operation frequency in two-
dimensional plane. Hence, utilized hybrid optimization
technique is composed of artificial neural network with Genetic
Algorithm, Spider Monkey Optimization and Grey Wolf
Optimizer in order to estimate feed position.
III. ARTIFICIAL NEURAL NETWORK
Artificial Neural Network (ANN) has ability to learn, use
memory the knowledge about the system. Moreover, it can
search, reproduce and discover new knowledge without any
help. A neural network is a natural propensity for storing
experiential knowledge. Also, it can prepares to use when they
are needed [21]. ANN is a computer program that simulates
biological neural networks. With these features, it can offer
effective solutions for optimization, classification, prediction,
pattern recognition, memory management and control Fig. 2. Pseudo-code of GA
problems.
In GA process, chromosomes are randomly generated for
initial population. At following iterations, crossing-over and
IV. ARTIFICIAL INTELLIGENCE ALGORITHMS mutation process are performed to obtain a new generation.
Crossing-over operation is used to make better solutions from
combination of different parts in others. Thanks to random
Metaheuristic algorithms are divided into three main changes in character string, a copy of individuals in the
groups: evolutionary, physics based and swarm intelligence previous generation prevent the transfer to the next generation
algorithm. Evolutionary algorithm is an adaptation of evolution via mutation process. Then, fitness values are determined for
events in nature for optimization algorithm. In 1992, Holland all chromosomes by comparison with each other. The better
proposed genetic algorithm which is the most popular and first new ones have fitness value, the more they have chance of
algorithm in this branch. Then, Differential Evolution, survival in the next generation.
Biogeography-Based Optimizer, Genetic Programming and
Evolution Strategy are some examples of evolution algorithm
[22-23]. Physics-based technique is the second subclass of B. Spider Monkey Optimization
metaheuristic algorithms. This kind of algorithms based on
imitate physical rules in nature. Gravitational Search, Charged Spider monkeys have fission-fusion social network which
system search, Artificial Chemical Reaction, Black Hole, Ray, includes temporary subgroups for larger communication
Small-World, Galaxy-based Search and Curved-spaced structure. Also, fission-fusion social mechanism provides food
algorithm are well-known optimization methods [24-31]. competition among members of smaller foraging groups. It is
Finally, the third main branch of meta-heuristics is the swarm necessary to divide into smaller groups in case adequate food
intelligence method. These type of algorithms usually mimics supply could not be found by female monkey leader. Although
the social behavior of swarm in natural atmosphere. Particle the number of individuals in main monkey groups can be up to
swarm, Ant colony, Artificial Bee Colony and Bat inspired 50, the size may be reduced to 3 or 4 [34-37]. The members in
optimization are the main examples of swarm intelligence all subgroups interact with internal and external individuals
method [32]. about food availability and territorial boundaries.
A. Genetic Algorithm In fission-fusion social structure, the foraging of spider
monkeys have four steps. At the first, monkey groups try to
search and find the food foraging. In the second stage, groups’
Genetic algorithm (GA) is a heuristics algorithm developed members update their location and calculate the distance from
for nonlinear problems [33]. GA could mimic evolutionary the food sources repeatedly in accordance with their group
processes observed in nature. In the complex multi- leader. Also, it is defined as an individual group leaders who
dimensional search space, it tries to search for the best solution have the best position in the group and replaced continuously
via the principle of having the best life. Variables of the in order to reach a better position. Therefore, other group
problem are represented as unique or group of gene in the individuals can direct in different directions to search for food.
chromosome. The most important factor in deciding the Finally, global leader has ever updated its best position under
success of genetic algorithms is the representation of stagnation condition.
individuals for solving the problem.
30
In Spider Monkey Optimization (SMO), Global Leader
Limit and Local Leader Limit are two major parameters to help
for taking leader decisions. Moreover, maximum group (MG)
and perturbation rate (pr) are other parameters to control
amount of groups and perturbation in current iteration.
SMO has a heuristic process which is based on a trial and
error. It starts to initialize population of N spider monkeys SMi
(i=1,2,3…. N) with D dimension vector. D denotes the number
of variables in optimization structure. SMi is the ith spider
monkey in the swarm and generated as:
SM ij SM min j U (0,1) (SM max j SM min j ) (8)
SMminj and SMmaxj are boundary for SMi of jth direction.
U(0,1) is a uniformly distributed random number between 0
and 1. The following step is Local Leader phase in which each
individual changes the position thanks to experience of local
group leader and members. The fitness value is computed
every new position of swarm member. If the fitness value of
new position is greater than former one, current position is Fig. 3. Pseudo-code of SMO
replaced with new one. The position update equation is
described as: C. Grey Wolf Optimizer
SMnewij SM ij U (0,1) ( LLkj SM ij ) U (1,1) (SM rj SM ij ) Grey Wolf Optimizer (GWO) is one of the novel swarm
intelligence method. It is inspired from grey wolves which
have 5-12 swarm members. They have a very dominant social
SMij is the ith SM member in the jth dimension, LLkj hierarchy for concept of search and hunt mechanism.
denotes the kth local group leader position. SMrj represents
randomly selected kth group member (r ≠ i). After the Local Grey wolf pack consists of alpha (α), beta (β), delta (δ)
Leader phase, the Global Leader phase embarked on a new wolf and omega’s (ω) wolves in Figure 4. Alpha wolf with
process. All of the SM’s members change their position by decisions give the pack directions to hunt. Although some
using experience of Global Leader. The position update is democratic behaviors are observed, alpha has strict authority in
implemented in this phase via following equation: the pack. Thanks to strong authority, the pack also has more
discipline. At the second best position in the social hierarchy is
beta grey wolf. In case of death or aging of the alpha wolf, beta
SMnewij SM ij U (0,1) (GLj SM ij ) U (1,1) (SM rj SM ij ) wolf is the best
leadership
candidate for the in the pack. It has
advisory role to alpha wolf and helps to discipline the pack. It
GLj is the global leader in the jth dimension. Next, the new is primarily responsible for providing coordination and
global and local leaders are necessary to be determined again. discipline between the alpha wolf and other members of wolf
A member with the best fitness value is proposed for global pack. Delta wolves in the third layer of the hierarchy have the
leader in entire population; on the otherhand, the best fitness task sharing for scouts, sentinels, elders, hunters and
values in each group is appropriate candidate for local leaders. caretakers. They help pack management in groups for the
hunting process. Moreover, they take care of the newborn and
the older members in the pack. Omega group is in the lowest
layer of the social hierarchy. It is required for them to become
increasingly powerful and social structure. They provide
candidate for the next generation of alpha, beta and gamma
wolves [38].
31
C 2r2 (14)
a is an algorithm component which is linearly changed
between 2 and 0. r1 and r2 are random vectors in valid interval
[0,1].
The best and latest three solutions are X1, X2 and X3 which
are saved as the best position of α, β, and δ. The next final
solution X(t+1) is defined as average of alpha, beta and delta
position. These expressions are formulated as:
D | C1.X -X|, D | C2 .X -X|, D | C3 .X -X| (15)
X1 X -A1 (X ), X 2 X -A 2 (X ), X 3 X -A 3 (X ) (16)
Fig. 4. Social Hierarcy of Grey Wolf X1 X 2 X 3
X(t 1) (17)
In the GWO, the alpha (α) is the fittest solution in the 3
search space. Then, beta (β) and delta (δ) is assigned as the In GWO algorithm, alpha, beta and delta are allowed to
second and third best solutions. The other solutions are in the update their location for right position. At the right iteration,
omega’s (ω) group. We consider the fittest solution as the they can attack towards the prey. With the above formulas,
alpha (α). The hunting mechanism is guided by α, β, and δ. The grey wolves gradually continue to scan the global space until
ω wolves follow and help them. GWO algorithm is basically they reach the optimum solutions in mathematical concept. If
divided into three main stages such as encircling, hunting and A<1, candidate solutions converge towards the prey; otherwise,
attacking prey. they diverge from it in Figure 6.
Grey wolves are coming close and surrounding the prey
before hunting. The following formula has been proposed to
model mathematically the encircling process in GWO
algorithm:
D C.X p (t)-X(t) (11)
X(t 1) X p (t ) A.D (12)
t is the current iteration indices. A and D are indicated
coefficient vectors. Xp and X are position vector of prey and a
grey wolf in Figure 5.
Fig. 6. Pseudo-code of GWO
V. PROPOSED METHOD
ANN consists of multiple neural cells with a combination.
Therefore, the concept of the neural cells will help in
Fig. 5. Position Updates In GWO understanding the entire network. Inputs, weights, biases and
outputs are main elements for neural network. Neural networks
The coefficient vectors are calculated as bellows: are separated into the layers such as input, hidden and output
layers in Figure 7. Conventional ANN is modified by Artificial
A 2a.r1 a (13) Intelligence Algorithms on the propose of update in weights
32
(Wi,k,j,o) and biases (BI,L1,L2,O). In this way, the hybrid network Fig. 7. Hybrid Artificial Neural Network Model
structure optimizes the outputs in lower error rate and
processing time.
VI. RESULTS AND DISCUSSION
In the proposed method, perceptron structure of artificial
neural network is used as objective function in the optimization
process. Weights and bias in artificial neural network structure To study the effects of changes in feeding position on the
are optimized as objective function. Genetic algorithm, Grey presented geometry, the antenna characteristics are
wolf optimizer and spider monkey optimization algorithms are investigated. The optimization algorithms are performed with
used to update weights and biases in the network structure. The AMD FX8 AMD 3.5GHz processors. 32GB of RAM with 4
main use of these algorithms is to prevent linerization in the GB of GDDR3 GeForce supported system memory with
network structure. The reason why the proposed algorithms are nVIDIA graphics card is used. Figure 8 indicates the
used is that the accuracy of each algorithm is different from simulation results about antenna design of conventional
that of the algorithm. According to mean error function method and proposed artificial intelligent algorithms for WIFI
obtained after each iteration, weights and biases of artificial communication.
neural network are updated in artificial intelligence algorithms. In order to train the proposed network, 4075 pieces of data
At the beginning of each algorithm, all weights and biases are were produced in the patches according to feed points spaced
randomly assigned. The parameters in optimization algorithms 0.1 mm step. Produced data includes band width, resonance
also start randomly at the start. This is to prevent proposed
frequency, return loss, x and y axis feed point. The constructed
algorithms from reaching local minimum solutions in search
hybrid artificial neural network consists of 10 hidden layers.
space.
The weights and biases in these hidden layers are updated with
In the proposed method, the inputs of the hybrid artificial artificial intelligence algorithms instead of the gradient descent
neural networks are composed of resonance frequency, band algorithm. The input 3 of the network consists of band width,
width and return loss. The network output is the antenna feed resonance frequency and return loss. At the output, 2-D feed
coordinates of the microstrip antenna in 2 dimensions. The point is tried to be obtained.
proposed method is a supervised algorithm and has training
and testing steps. Antenna parameter data was obtained by
changing the antenna feed point locations designed using the
HFSS program.
Fig. 8. Results of Antenna Design
The feeding points of the antenna were separately
determined via GA, SMO and GWO algorithms in order to
obtain the most appropriate return loss, operating frequency
and bandwidth. Computing feeding positions were respectively
(1.3230, 4.7317), (1.7770, 4.5424) and (1.8330, 4.6262) for
GA, SMO and GWO. The results in Table 1 were obtained
when the design parameters for the feed points were arranged
and antenna parameters were examined.
TABLE I. ANTENNA PARAMETERS OF EACH DESIGN
33
Resonance Return Bandwidth
Design Methods
Analyzing the data shown on Figure 8, some technical Frequency (GHz) Loss (dB) (MHz)
parameters of the antenna such as operating frequency, return Conventional
5.3204 -11.8998 40
Method
loss and bandwidth were extracted from Table 1. According to
the values given in Table 1, there are significant differences GA 5.4033 -25.8998 550
between conventional and other design methods. The best GWO 5.3756 -26.6732 550
return loss and the widest bandwidth are obtained by antenna SMO 5.3756 -36.0399 560
design of SMO algorithm. If we examine the resonance
frequencies obtained from the design methods, they are all very ACKNOWLEDGMENT
close together. The resonance frequencies obtained from the There is no conflict of interest between the authors
methods according to the order given in Table 1 are 5.32, 5.4,
5.37 and 5.37 GHz. When these frequencies are examined, it is
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