![International Journal of Trend in Scientific Research and Development (IJTSRD)
Volume 4 Issue 5, July-August 2020 Available Online: www.ijtsrd.com e-ISSN: 2456 – 6470
@ IJTSRD | Unique Paper ID – IJTSRD33020 | Volume – 4 | Issue – 5 | July-August 2020 Page 874
Performance Analysis of Different
Radiation Pattern using Genetic Algorithm
Rahul Pandya, Praveen Kumar Patidar
Department of Electronics and Communication,
Lakshmi Narain College of Technology (LNCT), Indore, Madhya Pradesh, India
ABSTRACT
In most applications of antenna arrays, side lobe levels (SLLs) are commonly
unwanted. Especially, the first side lobe level which determinesmaximumSLL
is the main source of electromagnetic interference (EMI),and hence, it should
be lowered. In this thesis proposed a very simple and powerful methodforthe
synthesis of linear array antenna. This method reduced the desired level of
side lobe level (SLL) as well as to steer the main beam at different-different
angle. In this paper we draw the radiation pattern for N = 24 elements with
main beam are shifted by 30° and the simulation result analysis by Matlab
R2013a tool.
KEYWORDS: Side Lobe Level (SLL), Electromagnetic Interference (EMI), Genetic
Algorithm (GA), Linear Array Antenna, Radiation Pattern
How to cite this paper: Rahul Pandya |
Praveen Kumar Patidar "Performance
Analysis of Different Radiation Pattern
using Genetic Algorithm" Published in
International Journal
of Trend in Scientific
Research and
Development(ijtsrd),
ISSN: 2456-6470,
Volume-4 | Issue-5,
August 2020, pp.874-
877, URL:
www.ijtsrd.com/papers/ijtsrd33020.pdf
Copyright © 2020 by author(s) and
International Journal of TrendinScientific
Research and Development Journal. This
is an Open Access article distributed
under the terms of
the Creative
Commons Attribution
License (CC BY 4.0)
(http://creativecommons.org/licenses/by
/4.0)
1. INTRODUCTION
Genetic algorithms belong to a stochastic class of
evolutionary techniques, whose robustness and global
search of the solutions space have made them extremely
popular among researchers. They have been successfully
applied to electromagnetic optimization, including antenna
design as well as smart antennas design. Communication,
radar and remote sensing systems employ thousands of
different types of antennas, and there is an increasing need
for them to be high-performance and customized.
Traditional methods of designing and optimizing antennas
by hand using simulation or analysis are time- and labor-
intensive, and limit complexity.
2. LITERATURE SURVEY
[1] Geng Sun et al. (2018), An Antenna Array Sidelobe
Level Reduction Approach through Invasive Weed
Optimization, Hindawi, International Journal of Antennas
and Propagation, Vol. 2018, The problems of synthesizing
the beam patterns of the linear antenna array (LAA)and the
circular antenna array (CAA) are addressed.
[2] Maryam Hesari et al. (2017), IntroducingDeeper Nulls
and Reduction of Side-Lobe Level in Linear and Non-
Uniform Planar Antenna Arrays Using Gravitational
Search Algorithm, Progress in Electromagnetics Research
B, Vol. 73, in this paper, a recently developed metaheuristic
algorithm, known as the Gravitational Search Algorithm
(GSA), is employed for the pattern synthesis of linear and
nonuniform planar antenna arrayswithdesiredpatternnulls
in the interfering directions and minimum side lobe level
(SLL) by position-only optimization.
[3] Saeed Ur Rahman et al. (2017), Analysis of Linear
Antenna Array for minimum Side Lobe Level,HalfPower
Beamwidth, and Nulls control using PSO, Journal of
Microwaves, Optoelectronics and Electromagnetic
Applications, Vol. 16 (2), This paper presents the
optimization performance of non-uniform linear antenna
array with optimized inter-element spacing and excitation
amplitude using Particle Swarm Optimization (PSO).
[4] V. S. Gangwar et al. (2015), Side Lobe Level
Suppression in Randomly Spaced Linear Array Using
Genetic Algorithm, 2015 IEEEInternationalMicrowaveand
RF Conference, this paper presents synthesis of randomly
spaced linear array (RSLA) with reduced side lobe level
(SLL).
3. Genetic Algorithms (GA)
The basic principles of genetic algorithms (GAs) and their
applications in computer systems were presented by
Holland and de Jong in 1975 and described in detail by
Goldberg. The GAstarts forming, usually by randommanner,
an initial population of chromosomes (individuals). The
performance of each individualis evaluated by the objective
function or the fitness function, which determinesthegoalin
IJTSRD33020](https://image.slidesharecdn.com/156performanceanalysisofdifferentradiationpatternusinggeneticalgorithm-200921055740/85/performance-analysis-of-different-radiation-pattern-using-genetic-algorithm-1-320.jpg)
![International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
@ IJTSRD | Unique Paper ID – IJTSRD33020 | Volume – 4 | Issue – 5 | July-August 2020 Page 875
each optimization problem. A high value on the objective
function implies a good chromosome.
A. Smart Antenna System (SAS)
Omni-directional or sectored antennas used in current
wireless communication systems, can be considered as an
inefficient use of power as most of it has been radiated in
other directions than toward the user. Signals that miss the
intended user will cause interference to other users in the
same or adjoining cells [1]. In the table 1 shown the
Relationships between Genetic Algorithm and Antenna
Parameters.
Table 1: Relationships between Genetic Algorithm and
Antenna Parameters
GA parameter Antenna array parameter
Gene Bit String (Ii, βi)
Chromosome One Element of Array
Individual One Array
Population Several Arrays After encoding
B. Side-lobes and smart antenna systems
No antenna is able to radiate all the energy in one preferred
direction. Someis inevitably radiated inotherdirections.The
peaks are referred to as side-lobes, commonly specified in
dB down from the main lobe, in figure 1 shows sidelobe and
main lobe.
Fig. 1: Side-lobes, Main-lobe and Back-lobe.
The smartantenna systems for cellular base stationscan be divided into three main categories,whichareillustratedinFigure2.
Switched Beam Systems.
Phased Arrays and
Adaptive Systems.
Fig. 2: (a) Switched Beam Systems, (b) Phased Arrays and (c) Adaptive Systems.
4. Simulation results for Radiation pattern when main beam is shifted by 30°
To evaluate the results of GA more objectively, at the same time, the pattern array antennas can be designed based on
Chebyshev methods withthe sameconditionswe have observed that from the classical arrayantennastheories,thelowestside
lobe level can be get using Chebyshev method on condition that it is given certain side lobe bandwidth. But It must satisfy the
condition of distance of array elements d 2.When d 2. Chebyshev method is not best, while GA can still work effectively.
Various results for different number of element as shown below in figure 3 to 8 with Radiation pattern when main beam is
shifted by 30° and the simulation parameters shown in table 2.
Table 5.1 Simulation Parameters
S. No. Parameter Description
1. Antenna array Linear antenna array
2. Optimization method Genetic Algorithm
3. Analysis Reduction of SLL for given main beam direction
4. Frequency 3GHz
5. Element spacing /2](https://image.slidesharecdn.com/156performanceanalysisofdifferentradiationpatternusinggeneticalgorithm-200921055740/85/performance-analysis-of-different-radiation-pattern-using-genetic-algorithm-2-320.jpg)
![International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
@ IJTSRD | Unique Paper ID – IJTSRD33020 | Volume – 4 | Issue – 5 | July-August 2020 Page 877
Fig. 8 Radiation Pattern for N = 30 element with main beam is shifted by 30°.
Table 3: Comparison of simulation results of different numbers of element with main beam is shifted by 30°.
S. No No of Elements Side lobe level Directivity
1 10 -12.9701 15.6234
2 12 -13.1571 16.546
3 16 -13.258 17.8193
4 20 -13.302 18.8037
5 24 -13.4625 19.6059
6 30 -13.5801 20.5627
5. Conclusion:
Today, the significance of the wireless communication is
known all over the world. In order to achieve the better
communication, many techniques and methods have been
introduced. Among these techniques, smart adaptive
antennas are trending topic in the research domain.
References
[1] Freweyni Kidane, Tawatu Abreha and DilipMali“Smart
Antenna Techniques for Interference Suppression in
WCDMA Using LMS Algorithm” TECHNIA –
International Journal of Computing Science and
Communication Technologies, Vol. 6(1), 2013.
[2] Mohammed A. Abdala and Areej K. Al-Zuhairy
“Integration ofSmart Antenna SysteminMobileAdHoc
Networks” International Journal of Machine Learning
and Computing, Vol. 3(4), 2013.
[3] Suraya Mubeen Dr. Am. Prasad and A. Jhansi Rani
“Smart Antenna its Algorithms and Implementation”,
IJARCSSE, Vol. 2(4), 2012.
[4] R. L. Haupt, “Thinned arrays using genetic algorithm”,
IEEE Transaction on Antenna and Propagation, Vol.12
(7), 1994, pp 993-999.
[5] R. L. Haupt, “Optimum quantized low sidelobe phase
tapers for array”, IEEE Electronics Lett. 31(14), 1995,
pp. 1117- 1118.
[6] S. A. Babale and D. D. Dajab, “Synthesis of a Linear
Antenna Array for Maximum Side-lobe Level
Reduction” International Journal of Computer
Applications, Vol. 85(16), 2014.
[7] Aniruddha Basak, Siddharth Pal, Swagatam Das and
Ajith Abraham, “Circular Antenna Array Synthesiswith
a Differential Invasive Weed Optimization Algorithm”,
10th International Conference on Hybrid Intelligent
Systems (HIS 2010), Atlanta, USA.
[8] Siddharth Pal, Anniruddha Basak, and Swagatam Das,
Ajith Abraham, “Linear Antenna Array Synthesis with
InvasiveWeed Optimization Algorithm”, International
Conference of Soft Computing and Pattern
Recognition”, IEEE, 2009.
[9] Karthick Ramachandran, “Smart Antennas in 4G”,
Innovative Systems Design and Engineering, Vol. 3(2),
2012.
[10] T. S. Jeyali Laseetha and R. Sukanesh, “Synthesis of
Linear Antenna Array using Genetic Algorithm to
Maximize Sidelobe Level Reduction”, International
Journal of Computer Applications, Vol. 20(7), 2011.
[11] R. L. Haupt, “Adaptive Nulling With Weight
Constraints”, Progress In ElectromagneticsResearchB,
Vol. 26, 2010, pp 23- 38.
[12] M. A. Panduro, “Design of Non-Uniform Linear Phased
Arrays usingGenetic Algorithms To Provide Maximum
Interference Reduction Capability in a Wireless
Communication System”, Journal of the Chinese
Institute of Engineers, Vol. 29(7), 2006, pp. 1195-1201](https://image.slidesharecdn.com/156performanceanalysisofdifferentradiationpatternusinggeneticalgorithm-200921055740/85/performance-analysis-of-different-radiation-pattern-using-genetic-algorithm-4-320.jpg)
performance analysis of different radiation pattern using genetic algorithm
- 1. International Journal of Trend in Scientific Research and Development (IJTSRD) Volume 4 Issue 5, July-August 2020 Available Online: www.ijtsrd.com e-ISSN: 2456 – 6470 @ IJTSRD | Unique Paper ID – IJTSRD33020 | Volume – 4 | Issue – 5 | July-August 2020 Page 874 Performance Analysis of Different Radiation Pattern using Genetic Algorithm Rahul Pandya, Praveen Kumar Patidar Department of Electronics and Communication, Lakshmi Narain College of Technology (LNCT), Indore, Madhya Pradesh, India ABSTRACT In most applications of antenna arrays, side lobe levels (SLLs) are commonly unwanted. Especially, the first side lobe level which determinesmaximumSLL is the main source of electromagnetic interference (EMI),and hence, it should be lowered. In this thesis proposed a very simple and powerful methodforthe synthesis of linear array antenna. This method reduced the desired level of side lobe level (SLL) as well as to steer the main beam at different-different angle. In this paper we draw the radiation pattern for N = 24 elements with main beam are shifted by 30° and the simulation result analysis by Matlab R2013a tool. KEYWORDS: Side Lobe Level (SLL), Electromagnetic Interference (EMI), Genetic Algorithm (GA), Linear Array Antenna, Radiation Pattern How to cite this paper: Rahul Pandya | Praveen Kumar Patidar "Performance Analysis of Different Radiation Pattern using Genetic Algorithm" Published in International Journal of Trend in Scientific Research and Development(ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-5, August 2020, pp.874- 877, URL: www.ijtsrd.com/papers/ijtsrd33020.pdf Copyright © 2020 by author(s) and International Journal of TrendinScientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by /4.0) 1. INTRODUCTION Genetic algorithms belong to a stochastic class of evolutionary techniques, whose robustness and global search of the solutions space have made them extremely popular among researchers. They have been successfully applied to electromagnetic optimization, including antenna design as well as smart antennas design. Communication, radar and remote sensing systems employ thousands of different types of antennas, and there is an increasing need for them to be high-performance and customized. Traditional methods of designing and optimizing antennas by hand using simulation or analysis are time- and labor- intensive, and limit complexity. 2. LITERATURE SURVEY [1] Geng Sun et al. (2018), An Antenna Array Sidelobe Level Reduction Approach through Invasive Weed Optimization, Hindawi, International Journal of Antennas and Propagation, Vol. 2018, The problems of synthesizing the beam patterns of the linear antenna array (LAA)and the circular antenna array (CAA) are addressed. [2] Maryam Hesari et al. (2017), IntroducingDeeper Nulls and Reduction of Side-Lobe Level in Linear and Non- Uniform Planar Antenna Arrays Using Gravitational Search Algorithm, Progress in Electromagnetics Research B, Vol. 73, in this paper, a recently developed metaheuristic algorithm, known as the Gravitational Search Algorithm (GSA), is employed for the pattern synthesis of linear and nonuniform planar antenna arrayswithdesiredpatternnulls in the interfering directions and minimum side lobe level (SLL) by position-only optimization. [3] Saeed Ur Rahman et al. (2017), Analysis of Linear Antenna Array for minimum Side Lobe Level,HalfPower Beamwidth, and Nulls control using PSO, Journal of Microwaves, Optoelectronics and Electromagnetic Applications, Vol. 16 (2), This paper presents the optimization performance of non-uniform linear antenna array with optimized inter-element spacing and excitation amplitude using Particle Swarm Optimization (PSO). [4] V. S. Gangwar et al. (2015), Side Lobe Level Suppression in Randomly Spaced Linear Array Using Genetic Algorithm, 2015 IEEEInternationalMicrowaveand RF Conference, this paper presents synthesis of randomly spaced linear array (RSLA) with reduced side lobe level (SLL). 3. Genetic Algorithms (GA) The basic principles of genetic algorithms (GAs) and their applications in computer systems were presented by Holland and de Jong in 1975 and described in detail by Goldberg. The GAstarts forming, usually by randommanner, an initial population of chromosomes (individuals). The performance of each individualis evaluated by the objective function or the fitness function, which determinesthegoalin IJTSRD33020
- 2. International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470 @ IJTSRD | Unique Paper ID – IJTSRD33020 | Volume – 4 | Issue – 5 | July-August 2020 Page 875 each optimization problem. A high value on the objective function implies a good chromosome. A. Smart Antenna System (SAS) Omni-directional or sectored antennas used in current wireless communication systems, can be considered as an inefficient use of power as most of it has been radiated in other directions than toward the user. Signals that miss the intended user will cause interference to other users in the same or adjoining cells [1]. In the table 1 shown the Relationships between Genetic Algorithm and Antenna Parameters. Table 1: Relationships between Genetic Algorithm and Antenna Parameters GA parameter Antenna array parameter Gene Bit String (Ii, βi) Chromosome One Element of Array Individual One Array Population Several Arrays After encoding B. Side-lobes and smart antenna systems No antenna is able to radiate all the energy in one preferred direction. Someis inevitably radiated inotherdirections.The peaks are referred to as side-lobes, commonly specified in dB down from the main lobe, in figure 1 shows sidelobe and main lobe. Fig. 1: Side-lobes, Main-lobe and Back-lobe. The smartantenna systems for cellular base stationscan be divided into three main categories,whichareillustratedinFigure2. Switched Beam Systems. Phased Arrays and Adaptive Systems. Fig. 2: (a) Switched Beam Systems, (b) Phased Arrays and (c) Adaptive Systems. 4. Simulation results for Radiation pattern when main beam is shifted by 30° To evaluate the results of GA more objectively, at the same time, the pattern array antennas can be designed based on Chebyshev methods withthe sameconditionswe have observed that from the classical arrayantennastheories,thelowestside lobe level can be get using Chebyshev method on condition that it is given certain side lobe bandwidth. But It must satisfy the condition of distance of array elements d 2.When d 2. Chebyshev method is not best, while GA can still work effectively. Various results for different number of element as shown below in figure 3 to 8 with Radiation pattern when main beam is shifted by 30° and the simulation parameters shown in table 2. Table 5.1 Simulation Parameters S. No. Parameter Description 1. Antenna array Linear antenna array 2. Optimization method Genetic Algorithm 3. Analysis Reduction of SLL for given main beam direction 4. Frequency 3GHz 5. Element spacing /2
- 3. International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470 @ IJTSRD | Unique Paper ID – IJTSRD33020 | Volume – 4 | Issue – 5 | July-August 2020 Page 876 Fig. 3: Radiation Pattern for N = 10 element with main beam is shifted by 30°. Fig. 4: Radiation Pattern for N = 12 element with main beam is shifted by 30°. Fig. 5: Radiation Pattern for N = 16 element with main beam is shifted by 30°. Fig. 6: Radiation Pattern for N = 20 element with main beam is shifted by 30°. Fig. 7: Radiation Pattern for N = 24 element with main beam is shifted by 30°.
- 4. International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470 @ IJTSRD | Unique Paper ID – IJTSRD33020 | Volume – 4 | Issue – 5 | July-August 2020 Page 877 Fig. 8 Radiation Pattern for N = 30 element with main beam is shifted by 30°. Table 3: Comparison of simulation results of different numbers of element with main beam is shifted by 30°. S. No No of Elements Side lobe level Directivity 1 10 -12.9701 15.6234 2 12 -13.1571 16.546 3 16 -13.258 17.8193 4 20 -13.302 18.8037 5 24 -13.4625 19.6059 6 30 -13.5801 20.5627 5. Conclusion: Today, the significance of the wireless communication is known all over the world. In order to achieve the better communication, many techniques and methods have been introduced. Among these techniques, smart adaptive antennas are trending topic in the research domain. References [1] Freweyni Kidane, Tawatu Abreha and DilipMali“Smart Antenna Techniques for Interference Suppression in WCDMA Using LMS Algorithm” TECHNIA – International Journal of Computing Science and Communication Technologies, Vol. 6(1), 2013. [2] Mohammed A. Abdala and Areej K. Al-Zuhairy “Integration ofSmart Antenna SysteminMobileAdHoc Networks” International Journal of Machine Learning and Computing, Vol. 3(4), 2013. [3] Suraya Mubeen Dr. Am. Prasad and A. Jhansi Rani “Smart Antenna its Algorithms and Implementation”, IJARCSSE, Vol. 2(4), 2012. [4] R. L. Haupt, “Thinned arrays using genetic algorithm”, IEEE Transaction on Antenna and Propagation, Vol.12 (7), 1994, pp 993-999. [5] R. L. Haupt, “Optimum quantized low sidelobe phase tapers for array”, IEEE Electronics Lett. 31(14), 1995, pp. 1117- 1118. [6] S. A. Babale and D. D. Dajab, “Synthesis of a Linear Antenna Array for Maximum Side-lobe Level Reduction” International Journal of Computer Applications, Vol. 85(16), 2014. [7] Aniruddha Basak, Siddharth Pal, Swagatam Das and Ajith Abraham, “Circular Antenna Array Synthesiswith a Differential Invasive Weed Optimization Algorithm”, 10th International Conference on Hybrid Intelligent Systems (HIS 2010), Atlanta, USA. [8] Siddharth Pal, Anniruddha Basak, and Swagatam Das, Ajith Abraham, “Linear Antenna Array Synthesis with InvasiveWeed Optimization Algorithm”, International Conference of Soft Computing and Pattern Recognition”, IEEE, 2009. [9] Karthick Ramachandran, “Smart Antennas in 4G”, Innovative Systems Design and Engineering, Vol. 3(2), 2012. [10] T. S. Jeyali Laseetha and R. Sukanesh, “Synthesis of Linear Antenna Array using Genetic Algorithm to Maximize Sidelobe Level Reduction”, International Journal of Computer Applications, Vol. 20(7), 2011. [11] R. L. Haupt, “Adaptive Nulling With Weight Constraints”, Progress In ElectromagneticsResearchB, Vol. 26, 2010, pp 23- 38. [12] M. A. Panduro, “Design of Non-Uniform Linear Phased Arrays usingGenetic Algorithms To Provide Maximum Interference Reduction Capability in a Wireless Communication System”, Journal of the Chinese Institute of Engineers, Vol. 29(7), 2006, pp. 1195-1201