The study of the ground plane effect on a Multiband PIFA Antenna by using Genetic Algorithm and Particle Swarm Optimization

Wakrim, L.; Ibnyaich, S.; Hassani, M. M.

doi:10.1590/2179-10742016v15i4626

L. Wakrim

I2SP Team, Faculty of science semlalia, Marrakech-Morocco

S. Ibnyaich

I2SP Team, Faculty of science semlalia, Marrakech-Morocco

M. M. Hassani

I2SP Team, Faculty of science semlalia, Marrakech-Morocco

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Fig.1
Genetic algorithm block diagram

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Fig.2
Flowchart of a PSO algorithm

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Fig.3
3D view geometry of PIFA antenna

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Fig.4
Gridded ground plane

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Fig.5
The combination block diagram of MATLAB with HFSS

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Fig.6
(a) Chromosome matrix, (b) Corresponding physical ground plane shape by using GA

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Fig.7
Optimal design of ground plane using GA

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Fig.8
Simulated reflection coefficient of the antenna with /without slot on the ground plane using GA

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Fig.9
2D Radiation pattern: (a) 3.5 GHz antenna without slots,(b)and (c) are 3.5GHz and 5.78GHz respectively for proposed antenna using GA

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Fig.10
Current distributions in the ground plane shaped by GA at (a) 3.5 GHz and (b) 5.78 GHz

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Fig.11
The input impedance Z in Ω versus frequency in GHz for the studied PIFA obtained by GA

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Fig.12
(a) Chromosome matrix, (b): Ground plane shape using PSO

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Fig.13
Geometry of the PIFA antenna obtained by PSO

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Fig.14
Simulated reflection coefficient of the initial antenna and obtained antenna by PSO

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Fig.15
2D Radiation pattern for antenna obtained by PSO: (a) 2D for f=3.5 GHz, (b) 2D for f=5.62GHz

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Fig.16
Current distributions in the ground plane shaped by PSO at (a) 3.5 GHz. (b) 5.62 GHz

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Fig.17
The input impedance Z in Ω versus frequency in GHz for the PIFA obtained by PSO

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Fig.18
Simulated reflection coefficient for the optimized antenna obtained by GA and PSO

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TABLE I
THE OVERALL DIMENSIONS OF THE ANTENNA (UNIT: mm²)

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TABLE II
SPECIFICATIONS OF PIFA ANTENNA

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TABLE III
NUMERICAL VALUES OF THE BANDWIDTH, REFLECTION COEFFICIENT, GAIN FOR THE OPTIMIZED AND REFERENCED

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TABLE IV
COMPARISON OF THE RESULTS AND OTHERS GIVEN BY LITERATURE

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Antenna components	Dimensions
Patch	38×24
Shorting pin	3×3.5
Feeding pin	2×0.5
Ground plane	40×41×0.5

Antenna characteristic	Specification
Patch	Rectangular
Resonant frequency	3.5 GHz
Bandwidth	4.11 %
Reflection Coefficient (S11)	−23.89 dB
VSWR	1.208
Gain	6.37 dB
Feeding method	Microstrip line

Antenna	Resonant frequency(GHz)	Bandwidth (%)	S₁₁(dB)	Gain (dB)
Reference antenna	3.5	4.11	−23.89	6.37
GA	f₁=3.5	19.31	−40.02	2.58
	f₂= 5.78	16.14	−38.74	5.65
PSO	f₁=3.5	30.57	−32.66	3.55
	f₂=5.62	37.90	−27.27	5.57

Paper	Antenna type	Method	Hight of antenna(mm)	Results
[³⁰[30] M. F. Abedin And M. Ali,2003, “Modifying The Ground Plane And Its Effect On Planar Inverted-F Antennas (PIFAs) For Mobile Phone Handsets”, IEEE Antennas And Wireless Propagation Letters, Vol. 2,]	Single and dual PIFA antenna	Meander-shaped ground plane	h=4	Reduce the hight of antenna by 50%
[³¹[31] R. Hossa, A. Byndas, And M. E. Bialkowski, June 2004, “Improvement Of Compact Terminal Antenna Performance By Incorporating Open-End Slots In Ground Plane”, IEEE Microwave And Wireless Components Letters, Vol. 14, No. 6]	Mono band PIFA antenna	Open-end parallel slots in the ground plane	h=6	Reduce the volume of the antenna Bandwidth enhancement
[³²[32] C. Picher And J. Anguera, 2009, ” Multiband Handset Antenna Using Slots On The Ground Plane: Considerations To Facilitate The Integration Of The Feeding Transmission Line”, Progress In Electromagnetics Research C, Vol. 7, 95–109.]	Dual band PIFA antenna	U shape slot on the ground plane	h=6	Bandwidth improved
[³³[33] Li-Jie Xu, Yong-Xin Guo, Wen Wu, 2012,” Dual-Band Implantable Antenna with Open-End Slots on Ground”, IEEE Antennas And Wireless Propagation Letters, Vol. 11,pp 1564 – 1567]	Multiband PIFA antenna	Two slots on the ground plane	h=1.27	Bandwidth enhancement
Our research	Dual band PIFA antenna	Gridded ground plane combined with GA/PSO	h=3	Reduce the ground plane shape by more than 50% Produce new resonant frequency Enhance the bandwidth

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The study of the ground plane effect on a Multiband PIFA Antenna by using Genetic Algorithm and Particle Swarm Optimization

Abstract