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Record W2773861676 · doi:10.1109/lwc.2017.2779483

Strategic Densification With UAV-BSs in Cellular Networks

2017· article· en· W2773861676 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 Wireless Communications Letters · 2017
Typearticle
Languageen
FieldEngineering
TopicUAV Applications and Optimization
Canadian institutionsCarleton University
FundersHuawei Technologies
KeywordsComputer scienceBase stationCellular networkSoftware deploymentWireless networkQuality of serviceField (mathematics)WirelessComputer networkReal-time computingDistributed computingTelecommunications

Abstract

fetched live from OpenAlex

Using base stations mounted on an unmanned aerial vehicle (UAV-BSs) is a promising new evolution of wireless networks for the provision of on-demand high data rates. While many studies have explored deploying UAV-BSs in a green field-no existence of terrestrial BSs, this letter focuses on the deployment of UAV-BSs in the presence of a terrestrial network. The purpose of this letter is twofold: 1) to provide supply-side estimation for how many UAV-BSs are needed to support a terrestrial network so as to achieve a particular quality of service and 2) to investigate where these UAV-BSs should hover. We propose a novel stochastic geometry-based network planning approach that focuses on the structure of the network to find strategic placement for multiple UAV-BSs in a large-scale network.

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 score0.185
Threshold uncertainty score0.652

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 science0.0010.000
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 head0.026
GPT teacher head0.226
Teacher spread0.200 · 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