Frequency-reconfigurable and high-gain metamaterial microstrip-radiating structure

Patel, Shobhit K.; Shah, Karan H.; Kosta, Y.P.

doi:10.1080/17455030.2018.1452309

Cited by 25 publications

(8 citation statements)

References 31 publications

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“…To generate a suitable pattern, the single PIN diode is intended to act in two distinct modes at different frequencies. They're employed in elements like cognitive radio systems, satellite applications, biomedical applications, and filters, among other applications 28 , 29 . By turning ON/OFF slots, the radiation pattern may be changed.…”

Section: Introductionmentioning

confidence: 99%

Machine learning assisted metamaterial-based reconfigurable antenna for low-cost portable electronic devices

Patel

Surve

Katkar

et al. 2022

Sci Rep

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Antenna design has evolved from bulkier to small portable designs but there is a need for smarter antenna design using machine learning algorithms that can meet today’s high growing demand for smart and fast devices. Here in this research, main focus is on developing smart antenna design using machine learning applicable in 5G mobile applications and portable Wi-Fi, Wi-MAX, and WLAN applications. Our design is based on the metamaterial concept where the patch is truncated and etched with a split ring resonator (SRR). The high gain requirement is met by adding metamaterial superstrates having thin wires (TW) and SRRs. The reconfigurability is achieved by adding three PIN diode switches. Multiple designs have been observed by adding superstrate layers ranging from one layer to four layers with interchanging TWs and SRRs. The TW metamaterial superstrate design with two layers is giving the best performance in gain, bandwidth, and the number of bands. The design is optimized by changing the path’s physical parameters. To shrink simulation time, Extra Tree Regression based machine learning model is used to learn the behavior of the antenna and predict the reflectance value for a wide range of frequencies. Experimental results prove that the use of the Extra Tree Regression based model for simulation of antenna design can cut the simulation time, resource requirements by 80%.

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Section: Introductionmentioning

confidence: 99%

Machine learning assisted metamaterial-based reconfigurable antenna for low-cost portable electronic devices

Patel

Surve

Katkar

et al. 2022

Sci Rep

Self Cite

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“…Microwave absorber and energy harvesting applications have been studied in a variety of frequency ranges (Alkurt et al, 2018; Dhillon and Mittal, 2019). MTMs are employed to increase antenna gain, directivity, bandwidth and reflection coefficient parameter of the antenna (Hasan et al, 2019; Patel et al, 2019; Ünal and Altıntarla, 2019). Also, it is possible to manipulate the wave by using MTMs as a polarization converter (Bakır et al, 2017a).…”

Section: Introductionmentioning

confidence: 99%

Chiral metamaterial-based sensor applications to determine quality of car lubrication oil

Dalgaç

Karadağ

Bakır

et al. 2020

Transactions of the Institute of Measurement and Control

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Motor oils have to be changed periodically in a period of 10.000–20.000 km according to the motor types. A chiral metamaterial sensor that operates in X band is developed to determine the quality of motor oils, numerically analyzed and experimentally tested in this study. The proposed design has square and circular shaped resonators that are printed on IS680 substrate. Reflection coefficient parameters of S11 and S22 are employed for the verification of sensor. The physical principle behind the structure in this study is based on the degradation of motor oil, which changes dielectric constant and causes resonance frequency shifts. According to S11 reflection coefficient data, 40 MHz(0 km–10000 km) and 60 MHz(0 km–5000 km) resonant frequency shifts are observed between clear and dirty motor oils samples. These shifts have the values of 30 MHz(0 km–10000 km) and 120 MHz(0 km–5000 km), when we look at S22. The simulated and experimental study results are complying with each other. The novel side of this study is to have high sensitivity and higher quality factor when it is compared with similar study results. Furthermore, no such studies have been conducted so far in the literature.

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“…MTMs A c c e p t e d M a n u s c r i p t are artificially engineered materials with electromagnetic characteristics not found in nature. These human-made materials are commonly used in antenna design to enhance the gain, bandwidth, and efficiency and recently to tilt the radiation beam [12][13][14]. In [15], SRR and H-shape were combined in a one-unit cell and used as an array to tilt the radiation beam of a horn antenna by an angle of +10°.…”

Section: Introductionmentioning

confidence: 99%

Reconfigurable metamaterial structure for 5G beam tilting antenna applications

Esmail

Majid

Dahlan

et al. 2020

Waves in Random and Complex Media

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In this paper, we propose a metamaterial (MTM) structure with a reconfigurable property designed to operate at the millimetre-wave (mm-wave) spectrum. Four switches are used to achieve the reconfigurable property of the MTM with two configurations. These two configurations exhibit different refractive indices, which used to guide the radiation beam of the antenna to the desired direction. The proposed planar dipole antenna operates at the 5th generation (5G) band of 28 GHz. The electromagnetic (EM) rays of the proposed antenna pass through different MTM configurations with different phases, subsequently results in the tilting of the radiation beam toward MTM configuration of high refractive index. Simulated and measured results of the proposed antenna loaded by MTM demonstrate that the radiation beam is tilted by angles of +34º and-31º in the E-plane depending on the arrangement of two MTM configurations onto the antenna substrate. Furthermore, the gain is improved by 1.7 dB and 1.5 dB for positive and negative tilting angles, respectively. The reflection coefficients of the antenna with MTM are kept below-10 dB at 28 GHz.

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Frequency-reconfigurable and high-gain metamaterial microstrip-radiating structure

Cited by 25 publications

References 31 publications

Machine learning assisted metamaterial-based reconfigurable antenna for low-cost portable electronic devices

Machine learning assisted metamaterial-based reconfigurable antenna for low-cost portable electronic devices

Chiral metamaterial-based sensor applications to determine quality of car lubrication oil

Reconfigurable metamaterial structure for 5G beam tilting antenna applications

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