Evidence map›Paper›PMID 41160592›Full record

ArticlePloS one2025

A stochastic wideband propagation-graph channel model for plants-affected drones air-to-ground mmWave communications.

Jiachi Zhang, Liu Liu, Shu-Bin Li, Yifei Xu

Abstract read
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

4 authors.

Jiachi ZhangSchool of Police Information, Shandong Police College, Jinan, China.ORCID https://orcid.org/0000-0002-9887-7032
Liu LiuSchool of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China.
Shu-Bin LiShandong Engineering Research Center of Intelligent Traffic Control and Guidance Technology for Public Security, Shandong Police College, Jinan, China.
Yifei XuSchool of Management Science and Engineering, Shandong University of Finance and Economics, Jinan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Unmanned aerial vehicles (UAVs) are experiencing extensive worldwide application across various fields, particularly in outdoor scenarios that often involve vegetation. A comprehensive understanding of the air-to-ground (A2G) wireless link channel and fading characteristics is crucial for the deployment and optimization of communication systems. In this paper, we propose a A2G wideband wireless channel model that integrates line-of-sight (LoS), reflection, and scattering propagation mechanisms at typical millimeter-wave (mmWave) bands based on the stochastic propagation-graph model. First, a geometric fractal tree modeling method is introduced to represent a single tree, the size and distribution of trees are modeled based on the stochastic theory. The propagation-graph (PG) model is then employed to simulate the A2G channel impulse response (CIR) at 28 GHz, taking into account maximal propagation delay constraints. On this basis, we investigate the spatial cross-correlation function (CCF) at different positions and effects of different UAV heights and circular movement radii on delay spread. Additionally, we study key small-scale statistical channel properties, including the power delay profile (PDP), power angular profile (PAP), and Doppler power spectrum density (DPSD). Our simulation results demonstrate that vegetation significantly impacts channel dispersion in spatial-temporal-frequency domains, and our model effectively captures the non-stationarity of UAV A2G channels.

Indexed as

Models, TheoreticalUnmanned Aerial DevicesWireless TechnologyStochastic Processes

Identifiers

PMID41160592
PMCPMC12571313

What OpenQuestion holds

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LicenceCC BY
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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.