一个以物理为基础的Airy束学习框架,用于避免在子特拉赫兹无线网络中的阻塞
Haoze Chen1, Atsutse Kludze1, Yasaman Ghasempour2
1Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ, USA.
Nature communications
|August 18, 2025
概括
空气光束可以克服在子特拉赫兹无线网络中的视线阻塞. 一个新的基于物理的机器学习框架优化了Airy束轨迹,提高了链接预算和网络覆盖范围.
科学领域:
- 无线通信无线通信
- 光学和光子学 在光学和光子学.
- 机器学习 机器学习
背景情况:
- 视线阻塞是子特拉赫兹无线网络的一个主要挑战.
- 亚特拉赫兹节点的近场范围扩大,使近场波面成形成为可能.
- 空气束提供自我加速特性和曲线轨迹,以规避阻塞.
研究的目的:
- 为了应对寻找最佳的Airy束轨迹的挑战,用于亚特拉赫兹无线网络.
- 开发一个基于物理的机器学习框架,用于有效的空气束造型.
- 调查工程Airy束在克服阻塞和扩大网络覆盖范围方面的潜力.
主要方法:
- 开发了一个基于物理的机器学习框架,集成近场电磁,射线光学和波光学.
- 使用开发的框架设计了空气束轨迹.
- 对拟议方法进行实验验证.
主要成果:
- 基于物理的机器学习框架成功地确定了最佳的Airy束轨迹.
- 经过实验验证,正确配置的Airy光束在高阻塞场景中显著增加了链路预算.
- 与传统的近场光束聚焦相比,表现出卓越的性能.
结论:
- 空气束,当通过物理知情机器学习进行最佳配置时,为阻塞的亚特拉赫兹无线网络提供了可行的解决方案.
- 这种方法提高了链接预算,扩大了覆盖范围,并减少了盲点.
- 该研究为在无线通信中实践实施曲线光束提供了关键的见解.
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