Chen Haoze, Kludze Atsutse, Ghasempour Yasaman
Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ, USA.
Nat Commun. 2025 Aug 18;16(1):7387. doi: 10.1038/s41467-025-62443-0.
The line-of-sight blockage is one of the main challenges in sub-terahertz wireless networks. Interestingly, the extended near-field range of sub-terahertz nodes gives rise to near-field wavefront shaping as a feasible remedy to tackle this issue. Recently, Airy beams emerged as one promising solution that opens significant opportunities to circumvent blockers with unique self-accelerating properties and curved trajectories. Yet, to unleash the full potential of curved beams in practice, one fundamental challenge remains: How to find the best beam trajectory? In principle, an infinite number of trajectories can be engineered. To find the optimal trajectory, we develop a physics-informed machine-learning framework for Airy beam shaping based on a detailed understanding of near-field electromagnetics, ray optics, and wave optics. The experimental results indicate that Airy beams, when correctly configured, can substantially increase the link budget under high-blockage scenarios even compared to near-field beam focusing, providing insight into coverage expansion and blind-spot reduction.
视距阻塞是太赫兹以下无线网络中的主要挑战之一。有趣的是，太赫兹节点扩展的近场范围使得近场波前整形成为解决这一问题的可行方法。最近，艾里光束成为一种有前景的解决方案，它具有独特的自加速特性和弯曲轨迹，为绕过障碍物带来了重大机遇。然而，要在实际中充分发挥弯曲光束的潜力，仍存在一个基本挑战：如何找到最佳光束轨迹？原则上，可以设计出无数条轨迹。为了找到最优轨迹，我们基于对近场电磁学、射线光学和波动光学的详细理解，开发了一个用于艾里光束整形的物理信息机器学习框架。实验结果表明，正确配置的艾里光束在高阻塞场景下甚至与近场光束聚焦相比，能显著增加链路预算，为覆盖扩展和盲点减少提供了思路。
