MétaCan
Menu
Back to cohort
Record W2896806510 · doi:10.1109/lcomm.2018.2876869

Outage Performance of UAV-Assisted Relaying Systems With RF Energy Harvesting

2018· article· en· W2896806510 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Communications Letters · 2018
Typearticle
Languageen
FieldEngineering
TopicUAV Applications and Optimization
Canadian institutionsUniversity of Victoria
FundersDepartment of Education of Guangdong ProvinceSoutheast UniversityNational Natural Science Foundation of China
KeywordsRician fadingComputer scienceFadingEnergy harvestingRayleigh fadingOutage probabilityMonte Carlo methodWeibull fadingEnergy (signal processing)Channel (broadcasting)Shadow mappingWirelessCommunications systemElectronic engineeringTelecommunicationsStatisticsEngineeringMathematics

Abstract

fetched live from OpenAlex

In this letter, we propose an UAV-based relaying system with energy harvesting functionality. In particular, we assume that this system is operated in urban communication environments, where the channel between the UAV and the land destination is modeled as shadowed-Rician fading or shadowed-Rayleigh fading. Based on this setting, outage probability analysis for different urban environment parameters is derived. Finally, Monte Carlo simulations are conducted to verify the accuracy of our analytical 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.

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 categoriesnone
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 score0.269
Threshold uncertainty score0.424

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)0.0000.000

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 head0.019
GPT teacher head0.210
Teacher spread0.191 · 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