Miniaturization of a Microstrip Patch Antenna with a Koch Fractal Contour Using a Social Spider Algorithm to Optimize Shorting Post Position and Inset Feeding

Abstract

This paper presents a social spider optimization (SSO) design of a small-size microstrip antenna. Two antenna miniaturization techniques, based on the use of a Koch fractal contour and a shorting post (connecting the patch to the ground plane), are combined to enable a major size reduction. The antenna is inset fed by a microstrip line. The developed SSO algorithm is used to find out the best radius and position of the shorting post and the length of the inset feed, to achieve the desired resonant frequency with good impedance matching. Antenna prototypes have been fabricated and measured. The good agreement obtained between numerical simulation and experimental results has validated the design procedure. Compared with a conventional rectangular patch, the antenna resonance frequency is reduced from 2.45 GHz to 730 MHz, which corresponds to a remarkable miniaturization of about 70%. The proposed antenna is suitable for applications in the 700-800 MHz frequency range, such as 4G mobile communication systems.

Numerical and Experimental Investigation of the Opposite Influence of Dielectric Anisotropy and Substrate Bending on Planar Radiators and Sensors

“…Actually, only the considered fractal resonators demonstrate relatively big resonance shifting due to the anisotropy of the substrates-Figure 17 (4-6%), while the pure bending effect is even smaller than in the case of the standard planar resonator (iteration i0)-Figure 20. We investigate the first and second iterations (i1, i2) of classical Koch fractal contours [50], performed on flat and bent substrates-Figure 19. The reason for the increased anisotropy influence is that the portions of the parallel E fields increase considerably with the iteration number of the fractal resonators, which provokes stronger resonance frequency shift down (when ε par > ε perp ).…”

Lessons Learned about the Design and Active Characterization of On-Body Antennas in the 2.4 GHz Frequency Band

“…A mismatch between the experimental results and simulations can be observed in Figures 7 and 8. Probable causes of the differences may be the losses attributable to the elements not considered in the design and simulation phase: uncertainties due to the manufacturing process, calibration or measurement errors, finite ground plane effects, influence of the metallic and electronic components of the device's complete hardware, properties of the transceiver and the communications protocol, but also other losses derived from the front-end of the measurement equipment, such as possible signal losses by impedance mismatch in the Tx or Rx antennas, in the coaxial cable that connects them to the spectrum analyzer or in the connectors [59][60][61][62][63][64]. It was not possible to perform any type of calibration to remove the losses associated with the transceiver interfaces, since it was not possible to establish any reference in the received signal if the battery-powered transceiver was used as transmitter.…”

“…Under normal circumstances, the standard method provides an effective way to evaluate antenna performance, with good correspondence between the simulation results and the experimental results. However, sometimes these differences can be important as a result of the loss of control in the elements that influence the antennas or external effects [60][61][62][63][64][65][66]. The proposed method, despite its higher complexity, assumes many of the effects that can influence the performance of the antennas, so it can represent a better approach to reality.…”

Compact ultra‐wideband FSS optimised through fast and accurate hybrid bio‐inspired multiobjective technique

“…These algorithms are inspired by the behaviour of biological systems and/or physical systems in nature, and they constitute powerful methods for solving complex problems in different areas of engineering and industry. The fact that these algorithms efficiently deal with arbitrary optimisation problems and are almost always self-organising, adaptable and tolerant of random defects [8], justifies the growing interest on the part of researchers in the electromagnetism community [9][10][11][12][13][14][15]. In antenna engineering, for example, BIC optimisation algorithms have been successfully applied to miniaturise a microstrip patch antenna with a Koch fractal contour using a social spider algorithm [9].…”

“…The fact that these algorithms efficiently deal with arbitrary optimisation problems and are almost always self-organising, adaptable and tolerant of random defects [8], justifies the growing interest on the part of researchers in the electromagnetism community [9][10][11][12][13][14][15]. In antenna engineering, for example, BIC optimisation algorithms have been successfully applied to miniaturise a microstrip patch antenna with a Koch fractal contour using a social spider algorithm [9]. This same algorithm was applied to the fast and accurate design and optimisation of FSSs and antennas in previous work [10], highlighting the use of artificial neural networks (ANNs) in a fast and accurate FSS project, as proposed in other work [11].…”