UWB Slot Antenna with Band-Notched Property with Time Domain Modeling based on Genetic Algorithm Optimization

Authors

  • Javad Zolghadr School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China
  • Yuanli Cai School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China
  • Nasser Ojaroudi Young Researchers and Elite Club, Ardabil Branch, Islamic Azad University, Ardabil, Iran

Keywords:

Band-notched function, GAP, microstripfed slot antenna, TDR analysis, UWB systems

Abstract

This paper presents a kind of band-notched slot antenna with time domain designing method for ultra-wideband (UWB) applications. The proposed antenna consists of a square radiating stub with a defected mircostrip feed-line using an M-shaped step impedance resonator (SIR) slot and a defected ground plane with a pair of inverted U-shaped slots. By cutting two inverted U-shaped slots in the ground plane, a new resonance at the higher frequencies is excited and hence much wider impedance bandwidth can be produced that. To generate a band-notched characteristic, we use an M-shaped slot at the feed-line. The proposed antenna can operate from 3.03 to 13.21 GHz with frequency band-notched function in 7.28-7.79 GHz to avoid interference from downlink of X-band satellite communication systems. To verify the validation of the proposed antenna an equivalent circuit based on time domain reflectometry (TDR) analysis is presented. In addition, the accuracy of the equivalent circuit models have been improved using Genetic algorithm optimization (GAP). Detailed simulation and numerical investigations are conducted to understand their behaviors and optimize for broadband operation. The proposed antenna exhibits almost omni-directional radiation patterns in UWB frequency range and could be used in wireless systems.

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References

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Published

2021-08-18

How to Cite

[1]
J. . Zolghadr, Y. . Cai, and N. . Ojaroudi, “UWB Slot Antenna with Band-Notched Property with Time Domain Modeling based on Genetic Algorithm Optimization”, ACES Journal, vol. 31, no. 08, pp. 926–932, Aug. 2021.

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