In this paper, we propose the use of GANs as learned, data-driven knowledge database that can be queried for rapid synthesis of suitable antenna designs given a desired response. As an example, we consider the problem of designing the Log-Periodic Folded Dipole Array (LPFDA) antenna for two non-overlapping ranges of Q-factor values. By representing the antenna with the vector of its structural parameters and considering each desirable range of the Q-factor as a class, we transform our problem to that of generating new samples from a given class. We develop two alternative models, a Conditional Wasserstein GAN and a label-switched library of vanilla Wasserstein GANs and train them with a dataset of features and their associated labels (parameter vectors and Q-factor range). The main component of these models is a generator network that learns to map a normally distributed noise vector along with a binary label to the vector of parameters of candidate structures. We demonstrate that in inference mode, these models can be relied upon for fast generation of suitable designs.

中文翻译：

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使用基于 GAN 的合成数据生成方法的天线设计

在本文中，我们建议将 GAN 用作学习的、数据驱动的知识数据库，在给定所需响应的情况下，可以查询该数据库以快速合成合适的天线设计。例如，我们考虑为两个不重叠的 Q 因子值范围设计对数周期折叠偶极子阵列 (LPFDA) 天线的问题。通过用其结构参数的向量表示天线并将 Q 因子的每个理想范围视为一个类，我们将问题转化为从给定类生成新样本的问题。我们开发了两个替代模型，一个条件 Wasserstein GAN 和一个普通 Wasserstein GAN 的标签切换库，并使用特征数据集及其相关标签（参数向量和 Q 因子范围）对其进行训练。这些模型的主要组成部分是一个生成器网络，它学习将正态分布的噪声向量与二进制标签一起映射到候选结构的参数向量。我们证明，在推理模式下，可以依赖这些模型来快速生成合适的设计。