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Record W3126764447 · doi:10.1109/lcomm.2021.3058189

Energy Efficient Subchannel and Power Allocation in Cooperative VLC Systems

2021· article· en· W3126764447 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Communications Letters · 2021
Typearticle
Languageen
FieldEngineering
TopicOptical Wireless Communication Technologies
Canadian institutionsMemorial University of Newfoundland
FundersAgencia Estatal de InvestigaciónNatural Sciences and Engineering Research Council of Canada
KeywordsVisible light communicationComputer scienceFractional programmingMathematical optimizationTransmitter power outputInterference (communication)Efficient energy useOptimization problemBandwidth (computing)Nonlinear programmingPower (physics)Quadratic programmingPower optimizationBandwidth allocationNonlinear systemComputer networkAlgorithmMathematicsTransmitter

Abstract

fetched live from OpenAlex

This letter studies the energy efficiency (EE) optimization of cooperative visible light communication (VLC) systems while considering the inter-cell interference and the lineof-sight blockage problems. Specifically, the optimization problem of subchannel and power allocation to maximize EE under transmit power budgets and users' minimum rate constraints is considered. The formulated problem turns out to be a difficult nonlinear fractional program for which a low-complexity iterative solution based on fractional programming theory and the quadratic transform approach is proposed. Extensive simulations are conducted to show the efficacy of the proposed scheme over conventional approaches. In addition, the outage analysis and impacts of varying the transmit power and the subchannel bandwidth on the EE performance are investigated.

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 score0.458
Threshold uncertainty score0.603

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 head0.017
GPT teacher head0.228
Teacher spread0.211 · 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