Energy Efficiency of Visible Light Communication using SEE-OFDM
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".