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Record W4390414012 · doi:10.1016/j.phycom.2023.102267

A multi-user cognitive radio full-duplex channel under imperfect spectrum sensing: Achievable rates and energy efficiency

2023· article· en· W4390414012 on OpenAlexfundno aff
Mohammad Ranjbar, Nghi H. Tran, Tutku Karacolak, Shivakumar Sastry, Yan Zhang

Bibliographic record

VenuePhysical Communication · 2023
Typearticle
Languageen
FieldEngineering
TopicFull-Duplex Wireless Communications
Canadian institutionsnot available
FundersAir Force Research LaboratoryOffice of Naval ResearchNatural Sciences and Engineering Research Council of CanadaMcGill UniversityNational Science Foundation
KeywordsCognitive radioComputer scienceChannel (broadcasting)False alarmTelecommunications linkEnergy (signal processing)GaussianInterference (communication)Efficient energy useTransmitter power outputImperfectAlgorithmTelecommunicationsTransmitterWirelessStatisticsElectrical engineeringMathematicsPhysicsArtificial intelligence

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.005
metaresearch head score (Gemma)0.021
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 score0.006
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.021
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0020.003
Science and technology studies0.0020.005
Scholarly communication0.0050.004
Open science0.0030.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.024
GPT teacher head0.267
Teacher spread0.242 · 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

Citations0
Published2023
Admission routes1
Has abstractno

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