PARALLEL COMPUTING IN GENETIC ALGORITHM FOR ADAPTIVE ARRAY ANTENNA * Department of Radiophysics, Faculty of Physics Vietnam National University, College of Science Hanoi, Vietnam thaolq
Trang 1PARALLEL COMPUTING IN GENETIC ALGORITHM FOR
ADAPTIVE ARRAY ANTENNA
* Department of Radiophysics, Faculty of Physics Vietnam National University, College of Science
Hanoi, Vietnam thaolq@vnu.edu.vn
+
Scientific and Technological Institutes of Military Radar Institute
17 Hoang Sam, Hanoi, Vietnam minh_viet08447@yahoo.com
Keywords: adaptive array antenna; genetic algorithm;
parallel computing; parallel genetic algorithm;
Abstract
This paper presents how parallel computing could apply into
genetic algorithm for adaptive array antenna There are some
constrains in this algorithm that prevent parallel computing to
reduce the benefits in time cost Therefore, instead of using
normal model of parallel computing, we apply parallel
genetic algorithm for adaptive array antenna to have an
interesting result which benefits in the deep NULL
1 Introduction
As the developed of technology, more and more wireless
devices are used Therefore, it’s possible to got interference
signal from undesired devices which located in some
directions These signals would reduce the desired signal and
make our device lost the information
In order to reject these signals, adaptive array antenna or
smart antenna is used more popular This antenna could
change its far-field pattern to put the NULL in some
directions that may have interference or undesired signals
This could be a parabolic or directional antenna which could
steer whole antenna to avoid these signals With this model,
we could only reduce a small part of noise
Phased array antenna is the evolution of adaptive array
antenna by its ability in changing far-field pattern with only a
changing in its phased shifting and amplitude weighting of
each element So with this kind of antenna, if we have a set of
suitable phased shifting and amplitude weighting, we could
put a deep NULL at any direction There are many algorithms
for computing these variables For example, Least Mean
Square Algorithm, Howells-Applebaum, which are
presented in [3, 5] But many researches [4, 6] have shown
that Genetic Algorithm is the best suitable for adaptive array
antenna because it could converse quickly and be easy to
apply in an array antenna with arbitrary number of elements
Parallel computing has opened new chance to reduce the computational cost in many difficult problems, such as databases, data mining, advance graphics and virtual reality, collaborative work environments … [2] It could be done on a multi-core computer or in a number of same computers in network or in Graphic Processing Unit (GPU) Applying this algorithm to genetic algorithm in adaptive array antenna would have some special results in time cost and characteristic of the far-field pattern
In this paper, we do parallel genetic algorithm for an adaptive array antenna in a computer cluster We also make comparison with the normal model of genetic algorithm to show the advantages of parallel genetic algorithm
2 Phased array antenna
Phased array antenna is a group of antenna elements which could digitally change the phased shifting and amplitude weighting These elements in the group can be arranged in many shaped to make many kinds of phased array antenna, for example, linear array antenna, nonlinear array antenna, circular array antenna, planar array antenna, etc For simple,
in this paper, we only discuss about linear array antenna (shown in figure 1) in which the elements are arranged in a line with equally distance
Trang 2The far field pattern of linear array antenna in mathematical
form is shown in the following equation:
¦
=
Ψ
= N
n
j n
n e w AF
1 Where:
presents the phased shifting and amplitude weighting for this
element
This variable depends on configuration of antenna
N: number of elements in the array
Base on this equation, we have the far-field pattern of a linear
array antenna with 20 elements in the form of figure 2
Changing the phased shifting or amplitude weight of each
element in this antenna would result to change the far-field
pattern in both main beam and side lope This is principle for
using this antenna in adaptive array antenna
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3 Genetic algorithm
Genetic algorithm (GA) is a global search and optimization
methods that simulate the metaphor of natural biological
evolution [2, 6] By generating a number of potential
solutions (or population) and applying the principle of
survival of the fitness, it produces successively better
approximations to a solution After a number of generations,
by the process of selecting and reproducing, it creates new
population which is better suited to the environment than the
initial Based on the idea, the flow chart of this algorithm is
created and shown in figure 3
In adaptive array antenna, we need to compute the phased
shifting and amplitude weighting for each element in whole
antenna in order to place a NULL in some directions that may
have interference signals To apply the genetic algorithm, we have to use a genome which contains all phased shifting and amplitude weighting of all elements in antenna This algorithm will reproduce new genome which present better optimized from initial one
4 Parallel computing
Parallel computing is an evolution of sequential computing [2] that base on the idea that many workers in a factory could make more products than only one In computing, we could split a huge computational cost to many parts and distribute to different processors to do at the same time (shown in figure 4)
By sharing the work between many processors, the time cost for computing could be speed up (S) a number of time which
is shown in Amdahl’s law [1]:
1 1
S
P P N
=
Trang 3It means that with a certain percentage of parallel work (P)
we could only increase the speed up to a limited times while
increasing the number of processors (N) (example in figure
5) Therefore, to get higher speed up number we should invest
in increasing the percentage of parallel work in whole
computation
However, the time cost in a parallel system is also affected by
receiving data) So total time needed for computing a problem
in parallel:
Therefore, for a problem in which the computational cost is
not too high and need so much time in distributing,
transmitting and receiving data, it is not benefited by applied
parallel computing For example, in genetic algorithm, we
have to do all these things in each generation with a finite
number of populations So if applied directly the parallel
computing in this problem the time cost would be increased
because of these required time
processors
Fortunately, we could use parallel genetic algorithm instead
of parallel computing in each generation It means we would
do genetic algorithm in each computer of processor then combine the population of from all computer by the process
of immigration There are more genes, especially good gene because we initialize and compute GA in different population
in different computers With this model (Figure 6), although
we couldn’t reduce the time cost, we would prevent the local minimized trap while putting NULL
5 Simulation result
Base on the theory, we have simulated the parallel genetic algorithm as well as normal genetic algorithm for a specific antenna in 2 different cases: only 1 interference signal and 4 interference signals This antenna and our genetic algorithm have some configuration shown in table 1
Distance between elements d/ λ 0.5
Table 1: This is an example of a table caption
Our simulation results are shown in the figure 7, 8, 9
Trang 4Figure 8 Simulation results: 1 NULL at -30 degree
From the simulation result, it’s obvious that parallel genetic algorithm has two advantages:
First, from the figure 8 and many other simulation results which is not mention in this article, the power in case of parallel genetic algorithm is often the same or higher than in normal genetic algorithm It means that parallel genetic algorithm produce the better suitable to the environment Second, in case of more than one NULL, the parallel genetic algorithm makes all the NULL to be deeper There isn’t any local minimized like in the case of normal genetic algorithm It’s the advantage of parallel genetic algorithm
Genetic algorithm produces the better suitable individual because it has more genes which are got from many other populations when initialized and applied genetic algorithm in different computer
6 Conclusion
This paper discusses about using parallel computing in adaptive array antenna Our simulation results have proved that parallel genetic algorithm is better in power and when there are more than one interference signals This problem is still opened and needed more research to figure out the best model Our future work will concentrate on how to reduce the time cost of this algorithm by apply parallel computing in cost function
References
[1] Amdahl, G.M “Validity of the single-prosessor aproach
to large scale computing capability”, AFIPS conference proceedings vol 30, Reston, Va., 1967, pp 483-485 [2] Blaise Barney, “Introduction to Parallel Computing”, Lawrence Livermore National Laboratory, 2010
[3] R T Compton, “Adaptive Antennas Concepts and Performance”, Englewood Cliffs, NJ: Prentice Hall,
1988
[4] R L Haupt, “Phase-only adaptive nulling with genetic
algorithms”, IEEE AP-S Trans 45(5) pp 1009–1015,
(June 1997)
[5] R A Monzingo and T W Miller, “Introduction to
Adaptive Antennas”, New York: Wiley,1980
[6] Le Quang Thao, Nguyen Ngoc Dinh, Dam Trung Thong,
“Amplitude and Phase Adaptive Nulling with a Genetic
Proceeding volume 3 pp.1887-1890, (2011)