Performance Analysis of RKHS Based Detectors for Nonlinear NLOS Ultraviolet Communications
Bibliographic record
Abstract
Ultraviolet (UV) communication has emerged as a promising solution for providing non-line-of-sight (NLOS) wireless connectivity due to strong molecular and aerosol scattering at UV wavelength. However, performance of UV based communication systems is severely impaired due to nonlinear transfer-characteristics of light emitting diode (LED), which degrades the overall symbol error rate (SER) performance. In addition, UV based communication systems are also impaired by multiplicative distortion due to turbulence, that causes detrimental instantaneous outages. Hence, in this work, first an expression for the outage probability is derived for a nonlinear UV communication system via analytical characterization of the statistics of the additive distortion. Further, utilizing the proposed analytical model for additive distortion, the error rate of reproducing kernel Hilbert space (RKHS) based detectors is quantified for the nonlinear outdoor NLOS UV channel. Additionally, using the derived expression for error-rate, an RKHS based minimum symbol error rate (MSER) equalizer is formulated to mitigate the distortion due to LED nonlinearity, and to enhance the error-rate performance of the considered nonlinear NLOS UV link. Convergence of the proposed MSER equalizer is analyzed, and improvements in error-rate promised by the proposed equalizer are validated by computer simulations over typical NLOS UV channels.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
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 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".