arXiv 2410.024742024-10-04eess.SP

通过均匀交叉熵优化实现多频段微带天线设计自动化

Automating the Design of Multi-band Microstrip Antennas via Uniform Cross-Entropy Optimization

Ali Al-Zawqari, Ali Safa, Gert Vandersteen

AI总结：

本文提出均匀交叉熵优化方法，用于根据目标S11曲线自动设计多频段微带天线，相比遗传算法和深度神经网络等现有方法在保证性能的同时将收敛时间缩短至约16分钟。

摘要：

微带天线设计自动化在过去十年中一直是活跃的研究领域。通过利用遗传算法（GAs）或近年来更常用的深度神经网络（DNNs）等机器学习技术，许多工作已经证明能够生成非平凡的、在面积利用方面高效或可用于复杂多频段场景的天线几何结构。然而，GAs和DNNs都以计算开销大而著称，通常需要数小时的运行时间才能生成新的天线几何结构。本文提出探索将交叉熵优化作为一种蒙特卡洛采样技术的新用途，用于在给定用户希望获得的目标$S_{11}$散射参数曲线条件下优化贴片天线的几何结构。我们将所提出的均匀交叉熵（UCE）方法与高斯过程、森林优化以及基线随机搜索等其他流行的蒙特卡洛优化技术进行了比较。我们证明所提出的UCE技术在性能上优于对比方法，同时仍具有合理的计算复杂度，约需16分钟即可收敛。最后，我们的代码以开源形式发布，希望对未来研究有所帮助。

英文摘要：

Automating the design of microstrip antennas has been an active area of research for the past decade. By leveraging machine learning techniques such as Genetic Algorithms (GAs) or, more recently, Deep Neural Networks (DNNs), a number of work have demonstrated the possibility of producing non-trivial antenna geometries that can be efficient in terms of area utilization or be used in complex multi-frequency-band scenarios. However, both GAs and DNNs are notoriously compute-expensive, often requiring hour-long run times in order to produce new antenna geometries. In this paper, we propose to explore the novel use of Cross-Entropy optimization as a Monte-Carlo sampling technique for optimizing the geometry of patch antennas given a target $S_{11}$ scattering parameter curve that a user wants to obtain. We compare our proposed Uniform Cross-Entropy (UCE) method against other popular Monte-Carlo optimization techniques such as Gaussian Processes, Forest optimization and baseline random search approaches. We demonstrate that the proposed UCE technique outperforms the competing methods while still having a reasonable compute complexity, taking around 16 minutes to converge. Finally, our code is released as open-source with the hope of being useful to future research.

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