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Record W4367838513 · doi:10.1109/sm57895.2023.10112375

Mobile Aerial Base Stations for Ultra-Reliable and Energy-Efficient Downlink Communications

2023· article· en· W4367838513 on OpenAlexaff
Yasser Nabil, Hesham ElSawy, Suhail Al–Dharrab, Hussein Attia, Hassan Mostafa, Ahmed Hamdy Khalil, Ibrahim M. I. Qamar

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicUAV Applications and Optimization
Canadian institutionsQueen's University
Fundersnot available
KeywordsTelecommunications linkBase stationComputer scienceInterference (communication)Transmission (telecommunications)Mobile stationComputer networkReal-time computingReliability (semiconductor)Channel (broadcasting)Mobile telephonyCellular networkStochastic geometryMobile deviceData transmissionMobile radioTelecommunications

Abstract

fetched live from OpenAlex

Mobile aerial base stations (BSs) for ultra-reliable device-centric downlink communication are proposed in this paper. BSs are carried on unmanned aerial vehicles (UAVs) that travel and transmit data to devices when they are hovering as close to the scheduled device as possible. The performance of the proposed system is compared to a network-centric downlink scheme in which stationary UAV-BSs communicate with the devices. The transmission success probability is derived using stochastic geometry, with the model taking into account the aggregate interference, trajectory, height, antenna directivity, air-to-ground channel, and others. Owing to its ultra-reliability, the device-centric scheme outperforms the network-centric one in terms of the downlink energy efficiency from the UAV perspective in addition to offering constant performance for all devices regardless of their locations.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.906
Threshold uncertainty score0.264

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 head0.013
GPT teacher head0.238
Teacher spread0.225 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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
Published2023
Admission routes1
Has abstractyes

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