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Accurate and Efficient Pattern Synthesis Using Generative Adversarial Network for Series-Fed Microstrip Antenna Array With New Elements

  • Kexin Chen
  • , Zengdi Bao*
  • , Yitao Liu
  • , Yang Li
  • *此作品的通讯作者
  • Beijing Institute of Technology

科研成果: 期刊稿件 › 文章 › 同行评审

摘要

Due to the unique geometry-dependent control of element amplitudes and phases, accurate and flexible pattern synthesis remains complex and challenging for series-fed microstrip antennas (SFMAs). In this communication, a modified physics-guided generative adversarial network (GAN) is developed to synthesize SFMA geometries for given pattern objectives. This synthesis framework offers higher efficiency and better synthesized patterns compared to traditional metaheuristic algorithms. Additionally, it does not require network pretraining. Moreover, new trapezoidal radiating elements acquiring ultralow reflection over a wide tuning range of coupling coefficients are proposed to maintain the traveling-wave mode, which is essential for accurate pattern control. Unlike other designs, these elements do not require additional reflection-canceling structures, thereby simplifying the antenna structure and synthesis process. Measured results of prototypes with a cosecant-squared (CSC2) pattern and a low-sidelobe pattern show excellent agreement with their respective pattern objective across 79–81 GHz, which validate the effectiveness of the synthesis method and the proposed element.

源语言英语
页(从-至)2833-2838
页数6
期刊IEEE Transactions on Antennas and Propagation
卷74
期3
DOI
出版状态已出版 - 2026

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