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Record W4413786065 · doi:10.1109/tgcn.2025.3603164

Fixed-Wing UAV Aided Full-Duplex Amplify-and-Forward Relaying With Constant Ambient Wind

2025· article· en· W4413786065 on OpenAlexaff
Xiaodong Ji, Ansheng YIN -, Jian-Feng Gu

Bibliographic record

VenueIEEE Transactions on Green Communications and Networking · 2025
Typearticle
Languageen
FieldEngineering
TopicFull-Duplex Wireless Communications
Canadian institutionsHealth Canada
FundersNational Natural Science Foundation of China
KeywordsWingDuplex (building)Constant (computer programming)Fixed wingAerospace engineeringComputer scienceEngineeringBiology

Abstract

fetched live from OpenAlex

This paper studies a fixed-wing unmanned aerial vehicle (UAV) aided full-duplex amplify-and-forward relaying in the presence of a constant ambient wind, where the UAV flies along a predetermined ground track and provides relaying service to two ground users so as to meet their demand of data transmission. According to aerodynamics and the wind triangle, engine power of the UAV under wind disturbance is evaluated. It is shown that the UAV’s engine power is related only to the vertical component of the wind-speed while not to the horizontal component when the UAV performs a level flight at a constant air-speed. On this basis, an optimization problem corresponding to the adjustment of the UAV’s air-speed, flight time and attitude (namely, crab and pitch angles) is studied. According to the wind triangle, three sub-problems are derived from the initial problem and solved by three propositions proposed, which leads to a novel optimization method. Computer simulation results confirmed that the developed optimization method can allow the UAV to fly according to the predetermined ground track and provide satisfactory relaying service for the two ground users through the adjustment of the UAV’s air-speed and flight time as well as its crab and pitch angles.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.662
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.235
Teacher spread0.217 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

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