Wave Iterative Computation for Fractal Microstrip Patch Antenna

Nuangpirom, Pinit; Klinbumrung, Kanyawit; Tangthong, Nipont; Akatimagool, Somsak

doi:10.1016/j.procs.2016.05.010

Cited by 5 publications

(2 citation statements)

References 3 publications

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“…The 4 renowned feeding approaches were the “microstrip line, coaxial probe (both contacting schemes), aperture coupling and proximity coupling (both non‐contacting schemes)”; nevertheless, their patterns differ based on frequency 16‐18 . Moreover, they reveal poorer back‐radiation and XP features 19‐21 …”

Section: Introductionmentioning

confidence: 99%

“…[16][17][18] Moreover, they reveal poorer back-radiation and XP features. [19][20][21] Commonly, the most well-known approaches for examining MPA were the cavity model, transmitting line model, and full-wave schemes. [22][23][24] The full-wave system is very flexible, precise and it takes care of the infinite and finite arrays, distinct components, random components, stacked, and coupling components.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid optimization model for design and optimization of microstrip patch antenna

Sarngadharan¹,

Narasimhamurthy²,

Sankaramoorthy

et al. 2022

Trans Emerging Tel Tech

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The antenna design for a specified resonant frequency necessitates the computation of optimal values of different sizes. This is a harder task for microstrip patch antenna (MPA) since there is no precise numerical formula that leads to accurate solutions for designing these antennas. Presently, bio‐inspired approaches are widely deployed in numerous antenna designs and it has revealed an immense assurance in handling the rising necessities of antenna engineering for overall cost, reduced size, and enhanced performances. This work aims to introduce an optimal MPA design, where the antenna elements like patch length, patch height, substrate width, and substrate length are optimally tuned by a new hybrid optimization model. For this, a new hybridized model known as shark smell integrated EHO is proposed. Eventually, the primacy of the suggested model is scrutinized via varied assessments.

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Section: Introductionmentioning

confidence: 99%