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Record W2524390134 · doi:10.21700/ijcis.2016.106

Energy Efficiency of Visible Light Communication using SEE-OFDM

2016· article· en· W2524390134 on OpenAlexvenueno aff
Samavia Rizwan, Nidhal Abdulaziz

Bibliographic record

VenueInternational Journal of Computing and Information Sciences · 2016
Typearticle
Languageen
FieldEngineering
TopicOptical Wireless Communication Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsVisible light communicationOrthogonal frequency-division multiplexingEnergy (signal processing)Light energyComputer scienceTelecommunicationsEnvironmental scienceOptoelectronicsPhysicsOpticsLight-emitting diode

Abstract

fetched live from OpenAlex

Wireless internet access technology can experience a major revolution through the use of breakthrough technology of Visible Light Communication (VLC) or Li-Fi (light fidelity) which could promise much higher data rates and better security and integrity of the data at the physical layer. There are many challenges being faced when visible light communication is being used resulting in low efficiency of the system. The main purpose of this paper is to endeavour to achieve highest data rate possible in an indoor environment while tackling problems which include loss of energy efficiency as OFDM is made unipolar and real-valued.A novel unipolar scheme called 'Spectral and Energy efficient' (SEE-OFDM) is proposed which is compatible with IM/DD wireless systems and is a great candidate for VLC. SEE-OFDM delivered promising results as it doubles the spectral efficiency of unipolar OFDM format. The fundamental idea is to generate multiple signals and sum them; both even and odd subcarriers are transmitted to carry information and the signal remains immune to interference. It is an attractive choice since higher data rates and SNR are gained, with a significant reduction in PAPR relative to conventional ACO-OFDM.

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: Empirical
Teacher disagreement score0.814
Threshold uncertainty score0.143

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 communication0.0000.001
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 head0.014
GPT teacher head0.273
Teacher spread0.259 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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