用机器学习算法预测基于THz频率应用的循环极化石墨烯介电共振器天线的性能,使用机器学习算法
Applied optics
|March 4, 2024
概括
这项研究介绍了用于太赫兹 (THz) 应用的石墨烯介电共振器天线. 机器学习准确地预测其性能,使无线系统的频率重新配置成为可能.
科学领域:
- 特拉赫兹 (THz) 技术的使用.
- 天线工程天线工程
- 材料科学是一种材料科学.
背景情况:
- 太赫兹 (THz) 频率模式为天线设计和模拟带来了独特的挑战.
- 石墨烯独特的电磁特性为先进的天线功能提供了潜力.
- 对于可适应的无线通信系统来说,频率重新配置是至关重要的.
研究的目的:
- 设计和分析一个以石墨烯介电共振器为基础的天线,运行在THz模式.
- 为了实现循环极化和频率重新配置.
- 利用机器学习来有效地预测THz天线的性能.
主要方法:
- 一个圆柱形陶振荡器天线的设计,用于循环偏振的扰动方形光圈.
- 将石墨烯加载集成到陶基板上,以实现频率重新配置.
- 应用人工神经网络 (ANN) 和随机森林算法进行性能预测,减轻大量的模拟时间.
主要成果:
- 拟议的天线在5.0和5.5 THz之间有效运行.
- 从5.1到5.35 THz实现了3 dB的轴比带宽.
- 在预测,测量和模拟的反射系数和轴系数之间观察到强烈的相关性,验证了机器学习方法.
结论:
- 石墨烯介电共振器天线表现出稳定的辐射特性和THz频段的良好多样性性能.
- 机器学习的整合大大降低了THz天线设计和分析的计算负担.
- 拟议的可重新配置天线适用于THz频率模式中的各种无线应用.
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