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antenna.pptx

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antenna.pptx

  1. 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. 2. Presentation Outlines  Motivation  Introduction  Proposed Work  Validation  Optimization Parameters  Optimization Results  References
  3. 3. Presentation Outlines
  4. 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. 5. Presentation Outlines
  6. 6. Introduction
  7. 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. 8. Presentation Outlines
  9. 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. 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. 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. 12. Proposed Work Yagi-Uda antenna design of 20 directors, CPW ground planes as reflectors, and CPS feeder
  13. 13. Presentation Outlines
  14. 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. 15. Presentation Outlines
  16. 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. 17. Presentation Outlines
  18. 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. 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. 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. 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. 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. 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. 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.

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