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Record W2741791149 · doi:10.1109/mnet.2017.1600280

UAV-Assisted Dynamic Coverage in a Heterogeneous Cellular System

2017· article· en· W2741791149 on OpenAlexaff
Yue Li, Lin Cai

Bibliographic record

VenueIEEE Network · 2017
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsMacrocellBackhaul (telecommunications)Computer scienceCellular networkComputer networkBase stationSoftware deploymentReuseFrequency reuseRelayResource allocationThe InternetOperating system

Abstract

fetched live from OpenAlex

The growing popularity of mobile Internet and massive MTC with special traffic characteristics and locations have imposed huge challenges to current cellular networks. Deploying new base stations, however, becomes difficult and expensive, especially for complicated urban scenarios and MTC traffic. The UAV-assisted heterogeneous cellular solution is proposed in this article. It utilizes UAV-based floating relay (FR) to deploy FR cells inside the macrocell, and thus achieves dynamic and adaptive coverage. Comprehensive analyses on FR cells' deployment including frequency reuse, interference, backhaul resource allocation, and coverage are given.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

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 science0.0000.001
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 head0.030
GPT teacher head0.270
Teacher spread0.239 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations149
Published2017
Admission routes1
Has abstractyes

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