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Record W4388016026 · doi:10.1109/eccs58882.2023.00017

Wind Effects on UAV-Based FSO Communications Under Doubly Inverted Gamma-Gamma Turbulence Channels

2023· article· en· W4388016026 on OpenAlexaff
Osamah S. Badarneh, Michel Kadoch

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicOptical Wireless Communication Technologies
Canadian institutionsÉcole de Technologie SupérieureUniversité du Québec à Montréal
Fundersnot available
KeywordsCumulative distribution functionProbability density functionTurbulenceMonte Carlo methodFree-space optical communicationGamma distributionPhysicsIrradianceChannel (broadcasting)Modulation (music)Bit error rateWind speedOptical communicationOpticsComputer scienceTelecommunicationsStatisticsMathematicsMeteorologyAcoustics

Abstract

fetched live from OpenAlex

The aim of this work is to study the effect of wind on the performance of unmanned aerial vehicle (UAV) based free-space optical (FSO) communications under a doubly inverted gamma-gamma (IGGG) turbulence channel. To this end, the irradiance probability density function (PDF) and cumulative distribution function (CDF) under random wind fluctuations are derived. Furthermore, considering intensity modulation/direct detection technique, closed-form analytical expressions for outage probability, average bit error rate, and average capacity are derived. The analytical results are verified through Monte-Carlo simulation results.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.274
Threshold uncertainty score0.999

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.002

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 head0.035
GPT teacher head0.262
Teacher spread0.227 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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