Kernel Recursive Maximum Versoria Criterion Based Post-Distorter for VLC Using Kernel-Width Sampling
Bibliographic record
Abstract
Visible light communication (VLC) has emerged as a potential candidate for next generation wireless communication systems. However, nonlinear characteristics of light emitting diode (LED), user-mobility, and DC-bias fluctuations are the major factors that limit the throughput of a VLC link, and makes the overall additive distortion as non-Gaussian distributed. To mitigate this non-Gaussian noise processes encountered in VLC systems due to LED nonlinearity, and user-mobility, recently a random Fourier features (RFF) based kernel recursive maximum Versoria criterion (KRMVC) based post-distortion algorithm is proposed, which delivers better performance as compared to the classical polynomial series, and kernel recursive least squares (KRLS) algorithms due to the incorporation of higher order statistics of error. However, the performance of RFF-KRMVC algorithm is sensitive to the choice of kernel-width, and results in approximation errors due to imperfect choice of kernel-width. This paper proposes a novel RFF-KRMVC algorithm using a kernel-width sampling (KWS) technique called as RFF-KWS-KRMVC, which implements the post-distortion under a hyperparameter-free finite memory budget. Furthermore, analytical expressions for mean square error, and error rate are quantified for the proposed RFF-KWS-KRMVC post-distorter, and corroborated by Monte-Carlo simulations performed over standard VLC channel models.
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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 | 0.004 |
| 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 | 0.001 |
| 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".