Implementation of 16 QAM signal transmission over RO-VLC system using high power LEDs
Bibliographic record
Abstract
Abstract White LEDs attract researchers for indoor visible light communication systems due to their good modulation characteristics, high efficiency, long lifetime, low cost, high tolerance to humidity, low power consumption, and minimal heat generation. Major challenges need to be resolved for LEDs including signal attenuation, ISI, and limited bandwidth for the proliferation of VLC systems. In this work, the challenges of signal attenuation and efficient utilization of limited bandwidth are addressed, keeping in mind the cost factor. A commercial high-power white phosphorous-coated LED with an optical preamplifier is used for a spectrally efficient 16 QAM Radio-Over-VLC link. The effect of varying the preamplifier gain on the system performance is investigated. Lower gain values limit the communication distance of the VLC system. A maximum transmission distance of 7 m considering a super-forward-error-correction (SFEC) limit of 1.863 × 10−2 could be achieved for a data rate of 0.5 Gbps. The transmission distance needs to be reduced to 6 m for a higher data rate of 0.6 Gbps with a resultant bit-error-ratio (BER) of 1.44 × 10−2 which is well within the SFEC limit. The achieved error vector magnitude (EVM), symbol error rate (SER), and Log bit-error-ratio (BER) metrics are used to characterize the VLC system performance.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".