Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 189

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

UAV Air-to-Ground Channel Characterization for mmWave Systems

Open access
Khawaja, Wahab, Ozdemir, Ozgur, Guvenc, Ismail·2017·2017 IEEE 86th Vehicular Technology Conference (VTC-Fall)

Abstract

Communication at mmWave bands carries critical importance for 5G wireless networks. In this paper, we study the characterization of mmWave air-to-ground (AG) channels for unmanned aerial vehicle (UAV) communications. In particular, we use ray tracing simulations using Remcom Wireless InSite software to study the behavior of AG mmWave bands at two different frequencies: 28 GHz and 60 GHz. Received signal strength (RSS) and root mean square delay spread (RMS-DS) of multipath components (MPCs) are analyzed for different UAV heights considering four different environments: urban, suburban, rural, and over sea. It is observed that the RSS mostly follows the two ray propagation model along the UAV flight path for higher altitudes. This two ray propagation model is affected by the presence of high rise scatterers in urban scenario. Moreover, we present details of a universal serial radio peripheral (USRP) based channel sounder that can be used for AG channel measurements for mmWave (60 GHz) UAV communications.

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/vtcfall.2017.8288376

Cites

1 of 1 references verified0 citation errors · 0 not checkable

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

Cites

Show all 1 references

Cited by

See all 189

Metadata

Authors
Khawaja, W., Ozdemir, O., & Guvenc, I.
Venue
2017 IEEE 86th Vehicular Technology Conference (VTC-Fall)
Publisher
IEEE
Published
2017-09
Pages
1-5
ISSN
1090-3038 (ISSN-L)
OpenAlex
W2736329051
Cited by
189
References
1
FWCI
29.96
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: UAV Air-to-Ground Channel Characterization for mmWave Systems. https://app.cochat.ai/papers/10.1109/vtcfall.2017.8288376
The paper
Khawaja, W., Ozdemir, O., & Guvenc, I. (2017). UAV Air-to-Ground Channel Characterization for mmWave Systems. 2017 IEEE 86th Vehicular Technology Conference (VTC-Fall). https://doi.org/10.1109/vtcfall.2017.8288376

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.