Ultra-Wideband Cavity-Backed Four-Arm Sinuous Antenna with Low
                        Height and Uniform Gain Characteristics

Kim, Dong-Hyun; Park, Chan Yeong; Kim, Young-Wan; Kim, Hyun; Yoon, Young Joong

doi:10.5515/kjkiees.2021.32.8.699

Cited by 1 publication

(1 citation statement)

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“…서 론 안테나는 무선 통신 시스템의 입단부와 종단부에서 데 이터가 포함된 전파를 송․수신하는 역할을 한다. 무선 통신에 적용되는 안테나의 경우, 범용적인 사용을 위한 일반적 구조의 안테나(예, 모노폴 안테나, 패치 안테나)와 특수 목적을 위해 설계된 복잡한 구조의 안테나(예, crooked wire antenna)가 활용된다 [1]∼ [3] . 다양한 무선통신 용 안테나 중 복잡한 구조를 가진 안테나를 설계하기 위 해서는 적용 분야에서 요구하는 안테나 성능이 구현되도 록 안테나 구조를 최적화해야 한다.…”

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Design of a Bent Wire Monopole Antenna Using Machine Learning

Park,

Ahn,

Choo

2022

J. Korean Inst. Electromagn. Eng. Sci.

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This study proposes a bent wire monopole antenna to achieve impedance matching in the frequency range of 950−1,050 MHz and a directive radiation pattern in the target direction (φ=180°, 40°<θ<50°). A machine learning technique based on a deep neural network is applied to obtain an optimal antenna design that is bent at three points to adjust the impedance and radiation pattern. The machine learning model is trained using a dataset that includes the antenna geometry and cost, evaluated on how well the antenna performance satisfies the target bandwidth and radiation pattern. After verifying that the machine learning model is trained using the figure of merit, the machine learning model is validated by comparing the cost estimated by the model with that estimated by a commercial electromagnetic simulator. Next, we obtain the optimal antenna design from the result of a grid search and fabricate it to verify its performance. The results show that the manufactured antenna has a matching bandwidth from 983.5 to 1,037.5 MHz and a radiation gain of approximately 4 dBi in the target direction. These results are in good agreement with the simulated performance of the antenna. Thus, we conclude that the antenna design obtained using the machine learning technique is valid for designing the proposed antenna.

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