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Mutual Coupling Reduction in Antenna Arrays Using Artificial Intelligence Approach and Inverse Neural Network Surrogates
Abstrakt
This paper presents a novel approach to reduce undesirable coupling in antenna arrays using custom-designed resonators and inverse surrogate modeling. To illustrate the concept, two stand-ard patch antenna cells with 0.07λ edge-to-edge distance are designed and fabricated to operate at 2.45 GHz. A stepped-impedance resonator is applied between the antennas to suppress their mutual coupling. For the first time, the optimum values of the resonator geometry parameters are obtained using the proposed inverse artificial neural network (ANN) model, constructed from the sampled EM-simulation data of the system, and trained using the particle swarm optimization (PSO) algorithm. The inverse ANN surrogate directly yields the optimum resonator dimensions based on the target values of its S-parameters being the input parameters of the model. The in-volvement of surrogate modeling also contributes to acceleration of the design process, as the ar-ray does not need to undergo direct EM-driven optimization. The obtained results indicate a re-markable cancellation of the surface currents between two antennas at their operating frequency, which translates into isolation as high as −46.2 dB at 2.45 GHz, corresponding to over 37 dB im-provement as compared to the conventional setup.
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Autorzy (7)
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Saeed Roshani
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Salah Yahya
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Muhammad Chaudhary
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Yazeed Ghadi
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Sobhan Roshani
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Pełna treść
- Wersja publikacji
- Accepted albo Published Version
- DOI:
- Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.3390/s23167089
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Informacje szczegółowe
- Kategoria:
- Publikacja w czasopiśmie
- Typ:
- artykuły w czasopismach
- Opublikowano w:
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SENSORS
nr 23,
ISSN: 1424-8220 - Język:
- angielski
- Rok wydania:
- 2023
- Opis bibliograficzny:
- Roshani S., Kozieł S., Yahya S., Chaudhary M., Ghadi Y., Roshani S., Gołuński Ł.: Mutual Coupling Reduction in Antenna Arrays Using Artificial Intelligence Approach and Inverse Neural Network Surrogates// SENSORS -Vol. 23,iss. 16 (2023), s.7089-
- DOI:
- Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.3390/s23167089
- Źródła finansowania:
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- COST_FREE
- Weryfikacja:
- Politechnika Gdańska
wyświetlono 33 razy