Impact of Ambient Light on Outdoor VLC Based Vehicular Movement
Bibliographic record
Abstract
Visible Light Communication (VLC) offers a sustainable and interference-free approach for outdoor sensing and navigation, particularly in applications such as smart farming and autonomous mobility. Despite its advantages of high bandwidth, energy efficiency, and compatibility with existing LED infrastructure, outdoor deployment remains challenging due to variations in sunlight and ambient illumination. This paper investigates how ambient light intensity, transmission distance, and receiver motion affect the performance of outdoor VLC links. Real irradiance datasets and simulation-based modeling are used to capture seasonal and diurnal variations and to quantify received power degradation under different lighting conditions. Results indicate that Ambient Light Intensity (ALI) is the dominant factor influencing link reliability, with significant power loss observed at midday and during high-irradiance periods, while lower illumination provides more stable connectivity. The findings emphasize the need for adaptive filtering, dynamic modulation, and receiver optimization to maintain communication stability. This work establishes a practical framework for designing resilient, energy-efficient VLC systems suited for outdoor environments, supporting applications in precision agriculture, vehicular networks, and sustainable infrastructure.
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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 | 0.002 |
| 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".