Machine Learning Assisted Indoor Visible Light Communication System
Bibliographic record
Abstract
Visible light communication (VLC) is an active and promising technology in the new world of wireless communications. Research has proved the importance of VLC technology shortly because of its ability to simultaneously illuminate and transmit data as it uses LEDs for transmission. However, the VLC channel sufferers from non-linearity, hence, this affects data transmission. On the other hand machine learning (ML) technology is known to solve non-linear problems. Therefore, the integration of the two technologies can drastically enhance the ability of VLC systems. In this research, three classification ML algorithm were employed in the system model. The performance of each ML algorithm is also analyzed, where the RF achieved a 97.4% prediction accuracy, and both the DT and SVM produced a 100% prediction accuracy. The system model design comprised of a MIMO indoor VLC system Matlab simulation, and a manual integration to the Python simulated ML algorithm.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".