MétaCan
Menu
Back to cohort
Record W3016171125 · doi:10.1109/lwc.2020.2986750

Multiuser Scheduling for Asymmetric FSO/RF Links in Satellite-UAV-Terrestrial Networks

2020· article· en· W3016171125 on OpenAlexaff
Huaicong Kong, Min Lin, Wei‐Ping Zhu, Mohamed‐Slim Alouini

Bibliographic record

VenueIEEE Wireless Communications Letters · 2020
Typearticle
Languageen
FieldEngineering
TopicSatellite Communication Systems
Canadian institutionsConcordia University
Fundersnot available
KeywordsTelecommunications linkComputer scienceChannel state informationScheduling (production processes)Rayleigh fadingFadingBeamformingComputer networkElectronic engineeringChannel (broadcasting)Real-time computingWirelessTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

This letter investigates the multiuser downlink transmission performance of an asymmetric free space optical (FSO)/radio frequency (RF) link. Here, the satellite delivers signal to the unmanned aerial vehicle (UAV) through a FSO link subject to Gamma-Gamma distributed turbulence, while the UAV forwards the decoded signal to multiple users through RF links characterzied by the correlated Rayleigh fading channel. By adopting that the selective decode-and-forward (DF) protocol at the UAV, we derive a closed-form expression for ergodic capacity (EC) of the considered system, where the RF link exploits transmit beamforming (BF) based on statistical channel state information (CSI) to obtain better performance than single antenna scenarios in existing works. Then, with the help of the derived EC and the available statistical CSI, a novel proportional fair scheduling (PFS) scheme is proposed. Finally, numerical results are conducted to verify the correctness of the theoretical analysis and the superiority of the proposed scheduling scheme.

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)0.001
metaresearch head score (Gemma)0.002
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: none
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.061
GPT teacher head0.275
Teacher spread0.214 · 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

Citations104
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Wireless Communications LettersSame topicSatellite Communication SystemsFrench-language works237,207