Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 1,182

The DOI matches its Crossref record.

Modeling air-to-ground path loss for low altitude platforms in urban environments

Open access
Al-Hourani, Akram, Kandeepan, Sithamparanathan, Jamalipour, Abbas·2014·2014 IEEE Global Communications Conference

Abstract

The reliable prediction of coverage footprint resulting from an airborne wireless radio base station, is at utmost importance, when it comes to the new emerging applications of air-to-ground wireless services. These applications include the rapid recovery of damaged terrestrial wireless infrastructure due to a natural disaster, as well as the fulfillment of sudden wireless traffic overload in certain spots due to massive movement of crowds. In this paper, we propose a statistical propagation model for predicting the air-to-ground path loss between a low altitude platform and a terrestrial terminal. The prediction is based on the urban environment properties, and is dependent on the elevation angle between the terminal and the platform. The model shows that air-to-ground path loss is following two main propagation groups, characterized by two different path loss profiles. In this paper we illustrate the methodology of which the model was deduced, as well as we present the different path loss profiles including the occurrence probability of each.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1109/glocom.2014.7037248

Cites

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cited by

See all 1,182

Metadata

Authors
Al-Hourani, A., Kandeepan, S., & Jamalipour, A.
Venue
2014 IEEE Global Communications Conference
Publisher
IEEE
Published
2014-12
Pages
2898-2904
ISSN
1930-529X (ISSN-L)
OpenAlex
W1988456768
Cited by
1,182
References
0
FWCI
33.72
Type
conference paper
Language
en
Open access
green

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Modeling air-to-ground path loss for low altitude platforms in urban environments. https://app.cochat.ai/papers/10.1109/glocom.2014.7037248
The paper
Al-Hourani, A., Kandeepan, S., & Jamalipour, A. (2014). Modeling air-to-ground path loss for low altitude platforms in urban environments. 2014 IEEE Global Communications Conference. https://doi.org/10.1109/glocom.2014.7037248

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.