Machine learning-driven design of dual-band antennas using PGGAN and enhanced feature mapping

doi:10.1049/mia2.12534

淡江大學機構典藏 > 工學院 > 電機工程學系暨研究所 > 期刊論文 > Item 987654321/126941

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題名:	Machine learning-driven design of dual-band antennas using PGGAN and enhanced feature mapping
作者:	Tuen, Lung-fai;Li, Ching-lieh;Chi, Yu-jen;Chiu, Chien-ching;Chen, Po-hsiang
關鍵詞:	dual-band antenna;hough transform;Latin hypercube sampling;PGGAN;WGAN-GP
日期:	2024-11-27
上傳時間:	2025-03-20 09:32:03 (UTC+8)
摘要:	This paper presents a systematic antenna design methodology that integrates machine learning, leveraging the progressive growth technique of Progressive Growing of GANs (PGGAN) to generate images of various dual-band PIFA-like antenna structures. The process involves using data augmentation methods to generate 4180 antenna samples. In the latent space, the authors employ Latin Hypercube Sampling to maintain diversity and combine it with the Hough Transform to enhance the edge features of the antennas while providing labelling functionality. This labelling method strengthens the relationship between antenna frequency and wavelength characteristics. The paper clearly outlines the design process, starting from the simulation of two types of single-frequency PIFA-like antennas (2.45 and 5.2 GHz, respectively) to the completion of PGGAN's generation task, resulting in a novel dual-band Wi-Fi PIFA-like antenna structure. The measurement results of the dual-band antennas exhibit excellent consistency with the simulation results.
關聯:	IET Microwaves, Antennas & Propagation 18(12),　p.1113-1138
DOI:	10.1049/mia2.12534
顯示於類別:	[電機工程學系暨研究所] 期刊論文

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