Evidence map›Paper›PMID 40825758›Full record

ArticleNature communications2025

A physics-informed Airy beam learning framework for blockage avoidance in sub-terahertz wireless networks.

Haoze Chen, Atsutse Kludze, Yasaman Ghasempour

Abstract read
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Haoze ChenDepartment of Electrical and Computer Engineering, Princeton University, Princeton, NJ, USA.
Atsutse KludzeDepartment of Electrical and Computer Engineering, Princeton University, Princeton, NJ, USA.ORCID http://orcid.org/0000-0001-6206-1165
Yasaman GhasempourDepartment of Electrical and Computer Engineering, Princeton University, Princeton, NJ, USA. ghasempour@princeton.edu.ORCID http://orcid.org/0000-0002-4494-499X

Funding

National Science Foundation (NSF) 2145240National Science Foundation (NSF) 2148271United States Department of Defense | United States Air Force | AFMC | Air Force Office of Scientific Research (AF Office of Scientific Research) FA9550-24-1-0144
6 · The paper itself

Abstract

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.

Identifiers

PMID40825758
PMCPMC12361386

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.