Adaptive Minimization of Direct Sunlight Noise on V2V-VLC Receivers
Bibliographic record
Abstract
Shot noise due to direct sunlight is the major cause of SNR degradation in vehicle to vehicle visible light communications (V2V-VLC) outdoors. This shot noise can be simply reduced by the receiver ‘looking away’ from the sun rays. However, this has to be done without reducing the received optical signal power, which is a 3-D tracking problem, where both transmitter and receiver are moving. This paper analyzes the resulting optimization problem and finds the optimal angle at which the instantaneous SNR is maximized. This depends on the incident angle of the optical signal as well as the incident angle of the sunlight. Note, the mobility adds random additive noise in addition to rapid angle variations. The paper uses an extended- Kalman filter to minimize the error and possible drift in the optimal angle in the presence of noisy measurements. This results in an adaptive SNR optimization system for real-time vehicle to vehicle visible light communications. We have also presented the analytical solution to the SNR optimization problem. Simulation results, on a curved freeway on a bright sunny day, demonstrate close matching between the actual optimal SNR and that achieved by the extended-Kalman filter.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".