MétaCan
Menu
Back to cohort
Record W4401548555 · doi:10.1002/mop.34293

Investigating orthogonal frequency division multiplexing using high‐power LED for visible light communication

2024· article· en· W4401548555 on OpenAlexaff
Rajat Paliwal, Piyush Patel, Ahmad Atieh

Bibliographic record

VenueMicrowave and Optical Technology Letters · 2024
Typearticle
Languageen
FieldEngineering
TopicOptical Wireless Communication Technologies
Canadian institutionsOptiwave Systems (Canada)
Fundersnot available
KeywordsVisible light communicationOrthogonal frequency-division multiplexingMultiplexingDivision (mathematics)Electronic engineeringPower (physics)Frequency-division multiplexingTelecommunicationsOptoelectronicsComputer scienceElectrical engineeringPhysicsLight-emitting diodeMaterials scienceEngineeringMathematics

Abstract

fetched live from OpenAlex

Abstract Spectrally efficient 16 quadrature amplitude modulation‐orthogonal frequency division multiplexing (QAM‐OFDM) system using specifications of commercially available high‐power phosphorous coated LED (PLED) is proposed in this study. To address the challenges of signal attenuation and limited bandwidth, systems integrated with a band pass filter (BPF) after the photodetector and optical preamplifier. The effect of BPF bandwidth and preamplifier gain on the performance of 16 QAM‐OFDM system have been investigated. Results demonstrated that 375 MHz bandwidth evaluated from 1.5 × symbol rate, offered an optimum performance achieving minimum bit‐error‐rate of 3.05 × 10 −4 . For preamplifier gain of 20 dB, we achieved a maximum distance of 3 m which is limited to 1.5 m for lower gain values. With BPF‐optimized bandwidth and a preamplifier gain of 20 dB, we have achieved a data rate of 5 Gbps for maximum distance up to 2 m while considering the super‐forward‐error‐correction limit of 1.863 × 10 −2 . With data rate of 6 and 7 Gbps, the distance lower down to 1.8 and 1.5 m, respectively.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.145
Threshold uncertainty score0.967

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.014
GPT teacher head0.246
Teacher spread0.232 · 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 designBench or experimental
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

Citations2
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueMicrowave and Optical Technology LettersSame topicOptical Wireless Communication TechnologiesFrench-language works237,207