Coverage performance analysis of genetic ALOGRATHIM controlled smart antenna system

1. Proceedings of 2010 IEEE Student Conference on Research and Development (SCOReD 2010),
13 - 14 Dec 2010, Putrajaya, Malaysia
Coverage Performance Analysis of Genetic
Algorithm Controlled Smart Antenna System
Mohammad Mehdi Badjian, KunalenThirappa ,Tiong Sieh Kiong,Johnny Koh Siaw Paw,
Prajindra Sankar Krishnan
Department of Electrical and Communication
Universiti Tenaga Nasional
Kajang, Malaysia
Email: Mehdi@uniten.edu.my.Kunalen@uniten.edu.my.Siehkiong@uniten.edu.my.JohnnyKoh@uniten.edu.my.
Sankar@uniten.edu.my
Abstract- In this paper, an innovative type of smart antenna
based on genetic algorithm (GA) has been developed and tested.
The new GA embedded solution simplifies the control of smart
antenna system with increment of the accuracy of beam coverage
and decrement of beam availability in compare with the
conventional switch beam smart antenna. Scanning antenna of
the system determined the location of mobile users through its
band passed radio frequency power detector circuitry. GA then
operates to optimize and steer the coverage beams to cater for
dynamic users' distribution with minimum power usage. The
experiment with GA embedded control system smart antenna
shows good result in beam formation and steering to handle
mobile users.
Keywords-component; Smart antenna, Genetic Algorithm,
Beam Forming, Beam steering, Beam Availability.
I. INTRODUCTION
The recent high demand of radio communication usage
results in implementation of smart antennas in wireless
communication applications for improving overall system
capacity, coverage, eliminating of interfering signals,
transmitter tracking and quality of reception and etc [1-3]. Such
system consists of an array of antenna elements followed by a
processor embedded artificial intelligent algorithm, which can
adjust and adapt its beam patterns in order to achieve main
goals of emphasizing the signal of interest and eliminating the
interfering signals.
The artificial intelligent algorithm used in smart antennas
varies but recently much attention is paid to genetic algorithm
(GA). GA was first proposed by Holland and Goldberg [4, 5]
has provided comprehensive overview and introduction to GA.
GA has been proven to be a powerful algorithm for system
identification, design and real time implementation [6]. It has
been developed based on genetic and evolutionary theory. GA
evolves solutions in an iterative manner by applying operators
to a pool of solutions.
Smart antennas are commonly used due to their
intelligence, and power consumptions. These antennas utilize
directional array elements and select a few elements from the
overall array for the beam forming stage. The selection process
is based on utilizing neighboring elements with the highest
level of receive power [7]. It has been shown that this approach
This work was supported by MOSTI (Ministry of Science, Technology
and Innovation Malaysia) with project code OI-02-03-SFOI51.
978-1-4244-8648-9/10/$26.00 ©2010 IEEE 81
results in satisfactory performance, similar to those obtained
using fully adaptive systems, while reducing the cost and
complexity of the antenna array system [8,9].
In this article, a new method of creating an innovative
smart antenna which is an upgrade to traditional switch beam
antennas with the ability of beam forming and beam steering
for handling mobile users using an embedded GA for decision
making has been studied. This solution is giving a better
response with less power consumption to users compare to
conventional switch beam antennas, and will be cost effective
and less complex compare to adaptive antennas.
II. GENETIC ALGORITHM
In this paper, a GA based search algorithm is proposed to
learn near optimal beam forming and beam steering in
WiMAX network. The main objectives of the proposed
scheme cover the following aspects:
• To handle all users in the service area;
• To maximize the traffic load;
• To minimize the power consumption by activating
minimum possible beams in the antenna;
Additionally there is a constraint that should be considered:
the beam forming and beam steering should have the
capability to handle future new users, so that the GA should be
working cooperatively with the antenna in real time basis to
cover the whole service area.
There are several steps that GA should go through in order
to bring the best solution for beam forming and beam steering.
In the first step, GA generates a random population of binary
chromosomes. Each chromosome in GA population represents
bits of the beam steering and beam on/off value. The goal of
GA is to perform the beam steering and beam forming by
adjusting these array settings. Since the algorithm must be
fast, the GA uses a small population size. Then in the iterative
manner new populations are created by application of
selection, crossover and mutation operations giving gradual
improvement in the best chosen chromosome.
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2. Representation of Beam
Angle and Status into
GA Chromosomes
Users Positions from
RF Power Detector
Random
Chromosomes
Generation
Beam Chromosome
Decoding to Angle &
Status
Results to Servo
Motor Controller
Figure I: flowchart of GA used for our innovative smart antenna
Many different structure of GA exist and few of them were
compared to find the most suitable one for this particular task
[10]. Fig. I shows the flowchart of GA used for our innovative
smart antenna.
Table 1 below, shows the parameter of GA used in this paper.
As shown in Table 1 the number of generation has been set to
100, so that the time for the GA to come up with the result is
becoming more into real time basis as there is the possibility
82
that users have the movement in the region of the antenna. The
length of the generation was chosen to be9 bits. This9 bits
includes 5 bits for the angle that the antenna should face to
and the other 4 bits for the beams activity (on/off) in the
antenna. The other parameters has been chosen based on the
system testing and trial and eroIT.
TABLE I. PARAMETERS OF GENETIC ALGORITHM
Selection Method Roulette wheel Selection
Crossover Type Random Multi-point crossover
Crossover Possibility 0.5
Mutation Type Random Multi-point crossover
Mutation Possibility 0.05
Generation Numbers 100
Individuals in Generation 20
Length of Generations 9 bits
A. Encoding, Decoding and Evolution Operators
First, an encoding scheme to represent antenna beam
forming and beam steering pattern as chromosomes has been
designed by receiving the location of users from the RF power
detector. Each individual in the population has the length of9
bits whose characteristics are coded by strings of zeros and
ones.
GA uses two evolution operators to generate new solutions
from existing ones: crossover and mutation. The crossover
divides the binary coding of each parent into two or more
segments and then combines to give a new offspring that has
inherited part of its coding from each parent. The crossover
possibility was set to 0.5 in our calculations and a random
multi-point crossover has been chosen for this research. The
mutation inverse bits in coding of the offspring's with a low
probability which was set to 0.05 for this research purpose and
a random multi-point mutation has been chosen for the
purpose of this research.
Additionally random selection does not ensure that a non­
dominated solution will survive in the next generation. Elitism
of few best chromosomes, which are copied to the next
population, is used to prevent losing the best found solutions
to date [11].
B. Optimize the Solution
In order to optimize the solution the GA needs a suitable
fitness function. There are several methods to evaluate the
fitness value of each individual. In this paper Eq (1) is used to
do so.
{(x) = L�a=l(uaxCua) + a �b=l(ubxCub)
+p L�=l(bxPb)
(1)
where in this equation, variables U, ua and ub are the total
number of users detected by the antenna and b is the number
of available beams in the antenna. The number of users
available is dependent on the location and time where the
number of beams available is set predefined and set to 4
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3. beams for our system to cover 120 degrees. Variables Cua
and Cub are the elements which are related to the possibility
of the user coverage, where this two variable can either be set
to one or zero. Cua sets to 0 if the user is having a good
coverage else, it sets to 1 and Cub sets to 1 if the user is
having weak coverage else it sets to 1. Constant Pb in the
other hand is set to value one when the beam is on and set to
zero when the beam is off Constant multipliers show the
importance of each equation in fitness function. As shown in
Eq( I ) the coverage of users which is the most important part
of fitness function is having the constant of 1, follows by the
weakly covered users coverage which has less importance and
has a constant of 0.15 followes by the antenna beam activity
(on/oft) which only affects the power consumption with the
constant of 0.08. The result to this fitness function should not
exceed 1 if so the population generated will be ignored and
those populations with the lower values near to zero have the
higher possibility of being chosen as the best population
generated.
The search for the best population continues for 100
generations, and the best individual will be chosen for the
beam steering and formation.
III. ARCHITECHTURE OF SMART ANTENNA
Structure of a system employing smart antenna and
receiver block diagram is shown in Fig. 2. Signal from each
radiating element Xn is decided to be ON or OFF by
application of complex weight Wn• Then all signals are
combined and fed to the receiver where they are interpreted as
required. Vector representation of the received signal can be
shown in equation (2).
(2)
Because Switch Beam antenna designed here is a four beam
antenna, therefore n which indicates the number of beam in
antenna will start from 1 and end by 4.
Antenna arrays has been designed such a way that all
elements are identical with radiating pattern defined withf(e)
having the beam-width size of 30 degrees, only when there are
no available users in the region of specific beam the GA
decides to set W to zero and that certain element will no longer
having the radiating pattern.
Figure 2: Structure of adaptive antenna array system
83
All antenna arrays are connected to a motor as shown in
block diagram in Fig. 3. The GA also decides what is the
angle lJ that radiating elements x, needed to be faced to. The
angle, lJ has been delivered from the best population of the
GA.
y I I Genetic
e Motor
y ....--<eX}--i1 Control systems
11+-----iL...-A_l_g_Or_it_h_m---J
Figure 3: Structure of adaptive antenna array system connected to the motor
control system
IV. EXPERIMENTAL RESULTS
In order to verify the validity of our proposed GA based
smart antenna system controller, our modified switch beam
smart antenna has been compared with a conventional switch
beam antenna, which has the same characteristics as our
antenna without the functionality of beam steering. Two
scenarios of low to medium traffic and medium to high traffic
have been studied. There are two conditions set for user
coverage. When the users are exactly in the beam-width of the
each beam of the antenna, they will be considered as having
the good coverage and when they lay on borders of two beam
beam-widths, they will be considered as week coverage.
Fig. 4 and Fig. 5 illustrate the comparison which has been
done for low to medium traffic[ I 2] on 20 sets of each
containing 200 users with different locations for both GA
assisted switched beam antenna and the conventional switch
beam antenna.
195
190
� r-II)
185'"
::J
<+-
0
ci 180
;z:
175
170
1 Simulation Senarios 20
• Conventional Switch Beam Antenna
• GA Based Smart Antenna
Figure 4: Comparison of good covered users in low to medium traffic scenario
between conventional switch beam antenna and innovative smart antenna
Other simulations has been done, and results obtained
where almost the same as what is shown in Fig. 4, Fig. 5, Fig.
6 and Fig. 7. As it is shown in Fig. 4, Fig. 5, Fig. 6 and Fig. 7,
the number of users been covered perfectly in the GA assisted
switched beam antenna is more compare to the one in the
conventional switch beam antenna resulting in the decrement
of the number of weak covered users in GA assisted switched
beam antenna. But comparing the low to medium traffic result
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4. to medium to high traffic result, it is obvious that the perfectly
covered users percentage has been dropped in medium to high
traffic situation.
25
20
'"
....
Q)
15'"
:J
.....
0
100 i==
;z:
5
0
1 Simulation Senarios 20
• Conventional Switch Beam Antenna
• GA based Smart Antenna
Figure 5: Comparison of weak covered users in low to medium traffic scenario
between conventional switch beam antenna and innovative smart antenna
290
285
280
t 275
:s 270
'0265
�260
255
250
245
1 Simulation Senarios 20
• Conventional Switch Beam Antenna
• GA Based Smart Antenna
Figure 6: Comparison of good covered users in medium to high traffic
scenario between conventional switch beam antenna and innovative smart
antenna
50
40
�
Q)
30'"
r-::-- ==:J
.....
==0
200
;z:
10
0
1 Simulation Senarios 20
• Conventional Switch Beam Antenna
• GA Based Smart Antenna
Figure 7: Comparison of weak covered users in medium to high traffic scenario
between conventional switch beam antenna and innovative smart antenna
84
Table 2 illustrates the average, rrummum and maximum
improvement in term of having perfect covered uses between
GA assisted switch beam antenna and the conventional switch
beam antenna in both scenarios of low to medium traffic and
medium to high traffic situations.
TABLE2. PERFECT USER COVERAGE IMPROVEMENT OF GA ASSISTED SWITCH
BEAM ANTENNA TO CONVENTIONAL SWITCH BEAM ANTENNA
Improvements Low to Medium traffic Medium to high traffic
Minimum 0% 0%
Average 1.55% 0.82%
Maximum 3.5% 2%
What makes the GA assisted switch beam antenna special
from the conventional switch beside the coverage
improvement is the percentage difference of activity status of
each beam in the antenna, anticipates in making the power
consumption at the GA assisted switched beam antenna to be
less compared to the conventional switch beam antenna. Fig.8
illustrates the comparison between conventional switched
beam antenna and our innovated smart antenna based on the
percentage availability (on) of each of 4 beams. Data shown in
this graph was obtained by testing 20 scenarios, each
containing 100 users with different location positions of users.
90%
80%
� 70%
:0
� 60%
'"
50%>
«
Vl
40%
3
� 30%Vl
E 20%'"
(l)
co 10%
0%
1 2 3 4
Beam number
• GA Assisted Switch Beam Antenna
• Conventional Switch Beam Antenna
Figure 8: Comparison of beam status availability between GA assisted switch
beam antenna and conventional switch beam antenna
V. CONCLUSION
In this paper we have explored the usage of genetic
algorithm for discovering a new technique of modifying the
conventional switch beam antenna. We have discussed the
design of genetic operator and how it can be used in beam
forming and steering for handling mobile users. Results
obtained from experiments done on the GA assisted switched
beam antenna, showed the less percentage activity of the
beams in the antenna anticipating the decrement in power
consumption together with the better performance in the
coverage of users compare to the conventional switched beam
antenna. Despite the small number of uses tested in our new
modified system the results show a better classification
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5. performance, which implies this technique can be a suitable
technique to be used for future usage of smart antennas.
ACKNOWLGMENT
This work was supported by MOST! (Ministry of Science,
Technology and Innovation, Malaysia) with project code 01-
02-03-SFOI 51.
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