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Record W4394967010 · doi:10.1109/tcsi.2024.3386365

Integrating OFDM Into Switching Power Supplies for Visible Light Communications

2024· article· en· W4394967010 on OpenAlexaff
Alireza Barmaki, Mehdi Narimani, Steve Hranilovic

Bibliographic record

VenueIEEE Transactions on Circuits and Systems I Regular Papers · 2024
Typearticle
Languageen
FieldEngineering
TopicOptical Wireless Communication Technologies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsVisible light communicationOrthogonal frequency-division multiplexingComputer sciencePower (physics)TelecommunicationsElectronic engineeringElectrical engineeringEngineeringPhysicsLight-emitting diode

Abstract

fetched live from OpenAlex

Visible light communications (VLC) is a promising technology that combines the ubiquity of illumination devices with broadband communication to provide connectivity. The most recent VLC communication standards specify orthogonal frequency division multiplexing (OFDM) as the underlying modulation due to its spectral efficiency and compatibility with existing broadband modems. However, a key challenge remains in realizing VLC modulators capable of generating OFDM signal compatible with the primary illumination function, while ensuring the efficacy of the luminaires. In this paper, a novel method is proposed to integrate OFDM modulation into a switching power supply. Commutating diodes in the DC/DC converter are replaced by light-emitting diodes (LEDs) which are used for communication while the larger illumination string of LEDs is left unmodulated preserving the quality of illumination and luminaire lifetime. Biasing the switching MOSFET to its saturation region during its off states allows for the MOSFET to act as an amplifier, amplifying the OFDM communication signal and injecting it to the light-emitting commutating diode (LECD). As a result, energy efficient LED drivers capable of OFDM signal transmission are realized. Both simulation and experimental results are provided to demonstrate the feasibility of this approach, showcasing superior performance compared to existing literature in terms of component count, efficacy, illumination quality, and reliability. Notably, based on simulation results, the efficacy of our approach surpasses the bias-T approach by approximately 6.25%.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.980
Threshold uncertainty score0.770

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 science0.0000.000
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 head0.015
GPT teacher head0.243
Teacher spread0.228 · 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 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

Citations2
Published2024
Admission routes1
Has abstractyes

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