Dimming Control in Visible Light Communication using RPO-OFDM and Concatenated RS-CC
Bibliographic record
Abstract
Increasing wireless data traffic is creating pressure on the conventional dwindling radio frequency spectrum. A new and reliable communication medium becomes a necessity. Visible Light Communication (VLC), a subset of optical wireless communication uses the visible light spectrum between 400 and 800 THz as a medium for communication. VLC utilizes the illumination of LED to establish a communication medium. The research focused on achieving a successful VLC communication link at low intensities of light without affecting the speed, accuracy and efficiency of VLC. The achievement of the paper was to devise a method to reduce the LED brightness, reducing energy consumption and most importantly maintain a reliable, efficient and successful VLC communication link at low intensities of LED. The research comprises of a Reverse Polarity Optical-Orthogonal Frequency Division Multiplexing (RPO-OFDM) modulator, a Forward Error Correction (FEC) encoder block that uses concatenated Reed Solomon - Convolutional Coding, a digital PWM dimming control circuit, an RPO-OFDM demodulator and a FEC decoder. The decoding is performed using the Berlekamp-Massey algorithm and the Viterbi algorithm. Extensive research on various modulation schemes, coding and error correction techniques along with various driver circuit design for dimming control in VLC were thoroughly investigated to conclude the best reliable solution for dimming control in VLC.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".