Edge-Aware, Data-Efficient Fine-Tuning of Progressive GANs for Multiband Antennas
學年 114
學期 1
出版（發表）日期 2025-11-22
作品名稱 Edge-Aware, Data-Efficient Fine-Tuning of Progressive GANs for Multiband Antennas
作品名稱（其他語言）
著者 Lung-Fai Tuen; Ching-Lieh Li; Yu-Jen Chi; Po-Han Chen
單位
出版者
著錄名稱、卷期、頁數 MDPI Electronics 14(23), p. 4574
摘要 This study proposes a data-efficient fine-tuning strategy for multi-band antenna synthesis using a Wasserstein Auxiliary-Guided Progressive Growing GAN (WAG-PGGAN). Starting from a pretrained 512 × 512 dual-band PIFA-like generator trained on 4180 samples at 2.45/5.2 GHz, we introduce three 3.5-GHz wideband seeds augmented to 836 images (new:legacy ≈ 1:5) and fine-tune only the highest-resolution stage on the combined 5016-image corpus. A Hough-transform-based edge-enhancement module with an edge-aware loss preserves conductor boundaries and strengthens frequency–geometry correlation. Across n = 8 fabricated prototypes, all achieve |S11| < −10 dB and collectively span 1.86–5.83 GHz; measured total efficiencies are 52–87% (e.g., 73.6% @ 2.68 GHz, 66.7% @ 3.56 GHz, 69.0% @ 5.83 GHz), with radiation patterns consistent with simulation. The method retains prior 2.45/5.2 GHz performance while adding 3.5-GHz wideband behavior using ≤ 17% new data (836/5016), demonstrating effective transfer from small datasets. On an RTX 3060 Ti, inference is ≈ 3 s/design after ~192 h of training. Simulation–measurement agreement confirms that fine-tuned WAG-PGGAN yields high-resolution, physically valid multi-band antennas with reduced data and computational cost.
關鍵字 PGGAN; WAG-PGGAN; edge-aware regularization; fine-tuning; data-efficient learning; multiband antenna; wideband; Hough transform; generative antenna design
語言 zh_TW
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期刊性質 國內
收錄於
產學合作
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審稿制度 否
國別 TWN
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出版型式 ,電子版
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機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/129300 )