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

doi:10.1049/mia2.12534

機構典藏 > College of Engineering > Graduate Institute & Department of Electrical Engineering > Journal Article > Item 987654321/126941

Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/126941

Title:	Machine learning-driven design of dual-band antennas using PGGAN and enhanced feature mapping
Authors:	Tuen, Lung-fai;Li, Ching-lieh;Chi, Yu-jen;Chiu, Chien-ching;Chen, Po-hsiang
Keywords:	dual-band antenna;hough transform;Latin hypercube sampling;PGGAN;WGAN-GP
Date:	2024-11-27
Issue Date:	2025-03-20 09:32:03 (UTC+8)
Abstract:	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.
Relation:	IET Microwaves, Antennas & Propagation 18(12),　p.1113-1138
DOI:	10.1049/mia2.12534
Appears in Collections:	[Graduate Institute & Department of Electrical Engineering] Journal Article

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