antenna

1. Presented By:
Amal Megahed Ahmed
Team Leaders:
Assoc. Prof. Ayman Ragab (Faculty of Engineering – Helwan University)
Dr. Ahmed Shaker (Faculty of Engineering – Helwan University)
HELWAN UNIVERSITY
FACULTY OF ENGINEERING
ELECTRONICS, COMMUNICATION AND COMPUTERS DEPARTMENT

2. Presentation Outlines
 Motivation
 Introduction
 Proposed Work
 Validation
 Optimization Parameters
 Optimization Results
 References

3. Presentation Outlines

4. Motivation
The movement to study and design in the
terahertz region is due to:
- The rapid growth of various wireless applications
and services.
- The required high data rate.
- The crowding of the microwave range.

5. Presentation Outlines

6. Introduction

7. Introduction (cont.)
Terahertz Advantages and Disadvantages
 Advantages:
• The huge bandwidth (0.1 THz to 10 THz) which increases the data
capacity.
• Free license range.
• For future wireless systems with data rates of more than 10 Gb/s.
• For indoor and short range wireless applications.
 Disadvantages:
• Atmospheric attenuation.

8. Presentation Outlines

9. Yagi-Uda antenna
 Actually, Yagi-Uda antennas are very appropriate for using in THz
applications over the other THz antennas because of the high directive
gain of the Yagi-Uda antennas to overcome the high atmospheric
attenuation in this band.

10. GSA-PSO Hybrid Technique
 In our work, the main idea is to integrate the ability of exploration in
GSA with the ability of exploitation in PSO to synthesize both
algorithms' strength.
 The GSA-PSO algorithm will be implemented in Matlab to optimize
the planar Yagi-Uda antenna design carried out using the HFSS
simulator for high gain and minimum return losses, simultaneously, at
300 GHz.

11. GSA-PSO Hybrid Technique
 In GSA-PSO, all agents in the first iteration are randomly initialized.
Then, gravitational force, gravitational constant, and resultant forces
among agents are calculated. After that, the accelerations of particles
are defined.
 Then, the velocities of all agents can be calculated based on the
selected best solution. Finally, the positions of agents are updated. The
process of updating velocities and positions will be stopped by meeting
an end criterion.

12. Proposed Work
Yagi-Uda antenna design of 20 directors, CPW ground planes as
reflectors, and CPS feeder

13. Presentation Outlines

14. Validation
280 290 300 310 320
-16
-14
-12
-10
-8
-6
-4
-2
Frequency (GHz)
S11
(dB)
Experimental [4]
Simulated [4]
Our simulation result

15. Presentation Outlines

16. Optimization parameters
 In this design, the directors’ positions and lengths (l1, l2, l3, ... , l20, d1, d2,
d3, ... , d20) are the optimization variables with a view to increase the
antenna gain (G) and improve the return loss (S11) at f = 300 GHz.
 These parameters will be optimized within the assigned decision space
(±25% from the initial dimensions in [4]) and the evaluation number is
set to be 300.
 The objective function is considered as:
Objective Function= max[aG( f ) + B|S11( f )|]

17. Presentation Outlines

18. Optimization Results
290 295 300 305 310
-60
-50
-40
-30
-20
-10
0
Frequency (GHz)
S11
(dB)
GSA-PSO
GA
Obtained Result [4]
Gradient

19. Optimization Results
 The S11 in GSA-PSO reached to -45.27 dB which is more improved
nearly by 24 dB and 17.4 dB than the GA and the gradient algorithm,
respectively, and by 34.7 dB than the conventional design.

20. Optimization Results
290 295 300 305 310
14
14.5
15
15.5
16
16.5
Frequency (GHz)
Gain
(dB)
GSA-PSO
GA
Obtained Result [4]
Gradient

21. Optimization Results
 The GSA-PSO gain is nearly equals to GA and Gradient gains and
reaches to 15.8 dB which is higher than the simulated conventional
design gain by 0.4 dB.
 For the 3D gain patterns, it is clear that the field intensity in the back
direction is lower for the optimized antenna using GSA-PSO, GA and
gradient than the conventional design.

22. Optimization Results
5 10 15 20 25 30
0.5
0.6
0.7
0.8
0.9
1
Iteration Number
Normalized
Objective
Function
GA
GSA-PSO
Gradient

23. Optimization Results
 The GSA-PSO has the ability to improve its performance throughout
the iterations, unlike the GA, and gradient.
 The GSA-PSO improved its global search capability till iteration 27
when it is compared with the GA and gradient which stopped the
enhancement after iteration 13 and 7, respectively.
 On an average, the convergence improvements for the GA and gradient
stopped after 43.3%, 23.5% of the iterations. While the GSA-PSO
continued till 90%. This comparative study shows a powerful
convergence capability for the GSA-PSO technique compared with the
GA, and Gradient.

24. References
[1] Pierewicz, R., Jacob, M., Koach, M., Schoebel, J., and Kuner, T., “Performance
analysis of future multi gigabit wireless communication systems and THz frequency
with highly directive antennas in indoor environments,”, J. Sel.
Top.QuantumElectron, IEEE,” 2008, pp. 421–430.
[2] Montaser, A.M., Mahmoud, K.R., and Elmikati, H.A., “Integration of an optimized
E-shaped patch antenna into laptop structure for bluetooth and notched-UWB
standards using optimization techniques,”, Applied Computational
Electromagnetics Society Journal,” 2012, pp. 786-794.
[3]Mirjalili, S., and Hashim, S. Z. M., "A new hybrid PSOGSA algorithm for function
optimization,”, 2010 International Conference, In Computer and Information
Application (ICCIA) ,” 2010, pp. 374-377.
[4] Pavanello, F., Ducournau, G., Peytavit, E., Lepilliet, S., and Lampin, J.F., “High-
gain Yagi–Uda antenna on cyclic olefin copolymer substrate for 300-GHz
applications,” ”, IEEE Antennas and Wireless Propagation Letters,” 2014.