Machine Learning-Based Channel Estimation in Visible Light Communication with Signal-Dependent Noise
Bibliographic record
Abstract
With the increase in mobile data use and the potential radio frequency (RF) spectrum shortage, we are nearing a significant RF spectrum shortage. To address this challenge, visible light communication (VLC) emerges as a potential remedy, albeit facing performance degradation due to signal-dependent shot noise (SDSN). This work presents a simple, yet efficient solution: a two-step artificial neural network-based (TSANN) estimator tailored for channel estimation in single-input singleoutput (SISO) VLC systems amidst SDSN presence. Initially, the estimator employs a low-complexity least-square methodology, refining its results through a predetermined neural network architecture. In addition, the study contrasts the performance of the TSANN estimator with traditional LS and maximum likelihood (ML) estimators, unequivocally showcasing the supremacy of TSANN. Furthermore, the study explores the influence of SDSN on the accuracy of TSANN estimation.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".