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Record W3200892586 · doi:10.1109/lcomm.2021.3114594

Intelligent Reflecting Surface-Aided Indoor Visible Light Communication Systems

2021· article· en· W3200892586 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
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsVisible light communicationComputer scienceTransmitterOrientation (vector space)MaximizationCommunications systemOptimization problemIterative methodMathematical optimizationAlgorithmTelecommunicationsMathematicsElectrical engineeringLight-emitting diodeEngineering

Abstract

fetched live from OpenAlex

This letter explores the use of intelligent reflecting surfaces (IRSs) to address the line-of-sight (LoS) blockage issue in an indoor visible light communication (VLC) system. This is done while considering practical user behaviors such as random receiver orientation and the presence of obstructions in the direct link between the transmitter and the receiver. Specifically, a system model for an IRS-aided VLC system is proposed and a rate maximization problem is considered to determine the optimal orientation of the IRS mirror array to establish robust non-LoS links. A low-complexity iterative solution based on the sine-cosine algorithm is proposed for this non-convex optimization problem. Simulation results are used to verify the effectiveness of the proposed IRS-aided VLC system design and optimization algorithm in overcoming the LoS blockage issue.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.355
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0030.001
Research integrity0.0000.001
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 head0.050
GPT teacher head0.295
Teacher spread0.245 · 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