MétaCan
Menu
Back to cohort

Cascaded Shadowed UAV-to-Ground Channels

2024· article· en· W4405909216 on OpenAlexaff
Remon Polus, Claude D’Amours

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicUAV Applications and Optimization
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

In this paper, cascaded shadowed uncrewed aerial vehicles (UAVs)-to-ground fading channels are introduced and mathematically characterized. This is done in order to provide a comprehensive statistical analysis by deriving expressions for their probability density function (PDF) and cumulative distribution function (CDF). This lays the groundwork for modeling and analyzing the reliability of wireless communication systems operating over such channels. Utilizing these expressions, key metrics such as the outage probability, average channel capacity, and average bit error probability are derived. The Meijer G function is employed to express these metrics in closed-form expressions. To validate our analysis, Monte-Carlo simulations are conducted, demonstrating the effectiveness and accuracy of our analytical framework. Index Terms-Beyond 5G communications, uncrewed aerial vehicles (UAVs), cascaded channels.

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: none
Teacher disagreement score0.965
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.009
GPT teacher head0.219
Teacher spread0.210 · 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
Published2024
Admission routes1
Has abstractyes

Explore more

Same topicUAV Applications and OptimizationFrench-language works237,207